Overview

Brought to you by YData

Dataset statistics

Number of variables79
Number of observations45421
Missing cells370315
Missing cells (%)10.3%
Total size in memory27.4 MiB
Average record size in memory632.0 B

Variable types

Numeric42
Text36
Unsupported1

Alerts

scrape_id has constant value "20250617032927" Constant
neighbourhood has constant value "Neighborhood highlights" Constant
has_availability has constant value "t" Constant
description has 1022 (2.3%) missing values Missing
neighborhood_overview has 25071 (55.2%) missing values Missing
host_location has 10781 (23.7%) missing values Missing
host_about has 20546 (45.2%) missing values Missing
host_response_time has 10393 (22.9%) missing values Missing
host_response_rate has 10393 (22.9%) missing values Missing
host_acceptance_rate has 9781 (21.5%) missing values Missing
host_is_superhost has 1569 (3.5%) missing values Missing
host_neighbourhood has 9507 (20.9%) missing values Missing
neighbourhood has 25070 (55.2%) missing values Missing
bathrooms has 8833 (19.4%) missing values Missing
bedrooms has 3063 (6.7%) missing values Missing
beds has 8876 (19.5%) missing values Missing
price has 9016 (19.8%) missing values Missing
calendar_updated has 45421 (100.0%) missing values Missing
has_availability has 3505 (7.7%) missing values Missing
estimated_revenue_l365d has 9016 (19.8%) missing values Missing
first_review has 12572 (27.7%) missing values Missing
last_review has 12572 (27.7%) missing values Missing
review_scores_rating has 12572 (27.7%) missing values Missing
review_scores_accuracy has 12580 (27.7%) missing values Missing
review_scores_cleanliness has 12580 (27.7%) missing values Missing
review_scores_checkin has 12588 (27.7%) missing values Missing
review_scores_communication has 12582 (27.7%) missing values Missing
review_scores_location has 12589 (27.7%) missing values Missing
review_scores_value has 12590 (27.7%) missing values Missing
license has 32539 (71.6%) missing values Missing
reviews_per_month has 12572 (27.7%) missing values Missing
maximum_maximum_nights is highly skewed (γ1 = 150.6950559) Skewed
maximum_nights_avg_ntm is highly skewed (γ1 = 180.1141711) Skewed
id has unique values Unique
listing_url has unique values Unique
calendar_updated is an unsupported type, check if it needs cleaning or further analysis Unsupported
bedrooms has 2580 (5.7%) zeros Zeros
beds has 554 (1.2%) zeros Zeros
availability_30 has 14424 (31.8%) zeros Zeros
availability_60 has 11189 (24.6%) zeros Zeros
availability_90 has 9364 (20.6%) zeros Zeros
availability_365 has 7199 (15.8%) zeros Zeros
number_of_reviews has 12572 (27.7%) zeros Zeros
number_of_reviews_ltm has 21476 (47.3%) zeros Zeros
number_of_reviews_l30d has 32927 (72.5%) zeros Zeros
availability_eoy has 8113 (17.9%) zeros Zeros
number_of_reviews_ly has 25072 (55.2%) zeros Zeros
estimated_occupancy_l365d has 21476 (47.3%) zeros Zeros
estimated_revenue_l365d has 14485 (31.9%) zeros Zeros
calculated_host_listings_count_entire_homes has 8536 (18.8%) zeros Zeros
calculated_host_listings_count_private_rooms has 30955 (68.2%) zeros Zeros
calculated_host_listings_count_shared_rooms has 44604 (98.2%) zeros Zeros

Reproduction

Analysis started2025-08-31 11:06:07.946490
Analysis finished2025-08-31 11:06:28.064707
Duration20.12 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct45421
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.490781734 × 1017
Minimum2708
Maximum1.44459909 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:28.423880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2708
5-th percentile6972777
Q137149372
median7.703369816 × 1017
Q31.199335788 × 1018
95-th percentile1.406832109 × 1018
Maximum1.44459909 × 1018
Range1.44459909 × 1018
Interquartile range (IQR)1.199335788 × 1018

Descriptive statistics

Standard deviation5.694867793 × 1017
Coefficient of variation (CV)0.8773777992
Kurtosis-1.692765714
Mean6.490781734 × 1017
Median Absolute Deviation (MAD)5.960668257 × 1017
Skewness-0.06092204823
Sum3.882685289 × 1018
Variance3.243151918 × 1035
MonotonicityNot monotonic
2025-08-31T11:06:28.644264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2708 1
 
< 0.1%
1.080579828 × 10181
 
< 0.1%
1.080733538 × 10181
 
< 0.1%
1.075777779 × 10181
 
< 0.1%
1.075810012 × 10181
 
< 0.1%
1.075827688 × 10181
 
< 0.1%
1.078403626 × 10181
 
< 0.1%
1.078491559 × 10181
 
< 0.1%
1.078550769 × 10181
 
< 0.1%
1.078583435 × 10181
 
< 0.1%
Other values (45411) 45411
> 99.9%
ValueCountFrequency (%)
2708 1
< 0.1%
2732 1
< 0.1%
2864 1
< 0.1%
6033 1
< 0.1%
6931 1
< 0.1%
ValueCountFrequency (%)
1.44459909 × 10181
< 0.1%
1.444598207 × 10181
< 0.1%
1.444587428 × 10181
< 0.1%
1.444544755 × 10181
< 0.1%
1.44450449 × 10181
< 0.1%

listing_url
Text

Unique 

Distinct45421
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:29.008728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length47
Mean length43.27269325
Min length33

Characters and Unicode

Total characters1965489
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45421 ?
Unique (%)100.0%

Sample

1st rowhttps://www.airbnb.com/rooms/2708
2nd rowhttps://www.airbnb.com/rooms/2732
3rd rowhttps://www.airbnb.com/rooms/2864
4th rowhttps://www.airbnb.com/rooms/6033
5th rowhttps://www.airbnb.com/rooms/6931
ValueCountFrequency (%)
https://www.airbnb.com/rooms/2708 1
 
< 0.1%
https://www.airbnb.com/rooms/18067 1
 
< 0.1%
https://www.airbnb.com/rooms/86945 1
 
< 0.1%
https://www.airbnb.com/rooms/256182 1
 
< 0.1%
https://www.airbnb.com/rooms/2864 1
 
< 0.1%
https://www.airbnb.com/rooms/6033 1
 
< 0.1%
https://www.airbnb.com/rooms/6931 1
 
< 0.1%
https://www.airbnb.com/rooms/7874 1
 
< 0.1%
https://www.airbnb.com/rooms/8021 1
 
< 0.1%
https://www.airbnb.com/rooms/255656 1
 
< 0.1%
Other values (45411) 45411
> 99.9%
2025-08-31T11:06:29.593650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 181684
 
9.2%
w 136263
 
6.9%
o 136263
 
6.9%
t 90842
 
4.6%
s 90842
 
4.6%
. 90842
 
4.6%
m 90842
 
4.6%
r 90842
 
4.6%
b 90842
 
4.6%
1 82916
 
4.2%
Other values (16) 883311
44.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1965489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 181684
 
9.2%
w 136263
 
6.9%
o 136263
 
6.9%
t 90842
 
4.6%
s 90842
 
4.6%
. 90842
 
4.6%
m 90842
 
4.6%
r 90842
 
4.6%
b 90842
 
4.6%
1 82916
 
4.2%
Other values (16) 883311
44.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1965489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 181684
 
9.2%
w 136263
 
6.9%
o 136263
 
6.9%
t 90842
 
4.6%
s 90842
 
4.6%
. 90842
 
4.6%
m 90842
 
4.6%
r 90842
 
4.6%
b 90842
 
4.6%
1 82916
 
4.2%
Other values (16) 883311
44.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1965489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 181684
 
9.2%
w 136263
 
6.9%
o 136263
 
6.9%
t 90842
 
4.6%
s 90842
 
4.6%
. 90842
 
4.6%
m 90842
 
4.6%
r 90842
 
4.6%
b 90842
 
4.6%
1 82916
 
4.2%
Other values (16) 883311
44.9%

scrape_id
Real number (ℝ)

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.025061703 × 1013
Minimum2.025061703 × 1013
Maximum2.025061703 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:29.795657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.025061703 × 1013
5-th percentile2.025061703 × 1013
Q12.025061703 × 1013
median2.025061703 × 1013
Q32.025061703 × 1013
95-th percentile2.025061703 × 1013
Maximum2.025061703 × 1013
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean2.025061703 × 1013
Median Absolute Deviation (MAD)0
Skewness0
Sum9.198032763 × 1017
Variance0
MonotonicityIncreasing
2025-08-31T11:06:29.956523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
2.025061703 × 101345421
100.0%
ValueCountFrequency (%)
2.025061703 × 101345421
100.0%
ValueCountFrequency (%)
2.025061703 × 101345421
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:30.110999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters454210
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2025-06-17
2nd row2025-06-17
3rd row2025-06-17
4th row2025-06-17
5th row2025-06-17
ValueCountFrequency (%)
2025-06-17 30374
66.9%
2025-06-18 15047
33.1%
2025-08-31T11:06:30.433999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 90842
20.0%
0 90842
20.0%
- 90842
20.0%
5 45421
10.0%
6 45421
10.0%
1 45421
10.0%
7 30374
 
6.7%
8 15047
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 454210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 90842
20.0%
0 90842
20.0%
- 90842
20.0%
5 45421
10.0%
6 45421
10.0%
1 45421
10.0%
7 30374
 
6.7%
8 15047
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 454210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 90842
20.0%
0 90842
20.0%
- 90842
20.0%
5 45421
10.0%
6 45421
10.0%
1 45421
10.0%
7 30374
 
6.7%
8 15047
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 454210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 90842
20.0%
0 90842
20.0%
- 90842
20.0%
5 45421
10.0%
6 45421
10.0%
1 45421
10.0%
7 30374
 
6.7%
8 15047
 
3.3%

source
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:30.591418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length11.77664516
Min length11

Characters and Unicode

Total characters534907
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcity scrape
2nd rowcity scrape
3rd rowprevious scrape
4th rowprevious scrape
5th rowcity scrape
ValueCountFrequency (%)
scrape 45421
50.0%
city 36602
40.3%
previous 8819
 
9.7%
2025-08-31T11:06:30.953323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 82023
15.3%
s 54240
10.1%
r 54240
10.1%
p 54240
10.1%
e 54240
10.1%
i 45421
8.5%
45421
8.5%
a 45421
8.5%
t 36602
6.8%
y 36602
6.8%
Other values (3) 26457
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 534907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 82023
15.3%
s 54240
10.1%
r 54240
10.1%
p 54240
10.1%
e 54240
10.1%
i 45421
8.5%
45421
8.5%
a 45421
8.5%
t 36602
6.8%
y 36602
6.8%
Other values (3) 26457
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 534907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 82023
15.3%
s 54240
10.1%
r 54240
10.1%
p 54240
10.1%
e 54240
10.1%
i 45421
8.5%
45421
8.5%
a 45421
8.5%
t 36602
6.8%
y 36602
6.8%
Other values (3) 26457
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 534907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 82023
15.3%
s 54240
10.1%
r 54240
10.1%
p 54240
10.1%
e 54240
10.1%
i 45421
8.5%
45421
8.5%
a 45421
8.5%
t 36602
6.8%
y 36602
6.8%
Other values (3) 26457
 
4.9%

name
Text

Distinct44036
Distinct (%)97.0%
Missing1
Missing (%)< 0.1%
Memory size355.0 KiB
2025-08-31T11:06:31.341387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length253
Median length77
Mean length36.81270365
Min length1

Characters and Unicode

Total characters1672033
Distinct characters1371
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43282 ?
Unique (%)95.3%

Sample

1st rowRun Runyon, Beaut Furn Mirror Mini-Suit w/ Firplc!
2nd rowZen Life at the Beach
3rd row* Beautiful Master Suite/Jacuzzi Tub/*
4th rowPoolside Serenity Studio
5th rowRUN Runyon, Beau Furn Rms Terrace Hollyw Hill View
ValueCountFrequency (%)
12449
 
4.5%
in 8383
 
3.0%
private 6342
 
2.3%
home 5005
 
1.8%
room 4509
 
1.6%
the 3961
 
1.4%
with 3934
 
1.4%
beach 3876
 
1.4%
bedroom 3573
 
1.3%
house 3476
 
1.3%
Other values (16794) 222459
80.0%
2025-08-31T11:06:31.974813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
233962
 
14.0%
e 134631
 
8.1%
o 116307
 
7.0%
a 97774
 
5.8%
t 85176
 
5.1%
i 84649
 
5.1%
n 76110
 
4.6%
r 74918
 
4.5%
l 56142
 
3.4%
s 48139
 
2.9%
Other values (1361) 664225
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1672033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
233962
 
14.0%
e 134631
 
8.1%
o 116307
 
7.0%
a 97774
 
5.8%
t 85176
 
5.1%
i 84649
 
5.1%
n 76110
 
4.6%
r 74918
 
4.5%
l 56142
 
3.4%
s 48139
 
2.9%
Other values (1361) 664225
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1672033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
233962
 
14.0%
e 134631
 
8.1%
o 116307
 
7.0%
a 97774
 
5.8%
t 85176
 
5.1%
i 84649
 
5.1%
n 76110
 
4.6%
r 74918
 
4.5%
l 56142
 
3.4%
s 48139
 
2.9%
Other values (1361) 664225
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1672033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
233962
 
14.0%
e 134631
 
8.1%
o 116307
 
7.0%
a 97774
 
5.8%
t 85176
 
5.1%
i 84649
 
5.1%
n 76110
 
4.6%
r 74918
 
4.5%
l 56142
 
3.4%
s 48139
 
2.9%
Other values (1361) 664225
39.7%

description
Text

Missing 

Distinct38345
Distinct (%)86.4%
Missing1022
Missing (%)2.3%
Memory size355.0 KiB
2025-08-31T11:06:32.423490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1000
Median length703
Mean length383.0039415
Min length1

Characters and Unicode

Total characters17004992
Distinct characters1546
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36626 ?
Unique (%)82.5%

Sample

1st rowRun Runyon Canyon<br /><br />Gym & Sauna <br /><br />Beautifully Furnished Mirrored Mini-Suite with Fireplace<br /><br />Central AC & heat<br /><br />Special Soaps<br /><br />Premium memory foam mattress & pillows<br /><br />First Morning: Complimentary Starbucks coffee, latte-style, protein bars, granola bars, a fresh-baked continental breakfast<br /><br />Welcome bottle of artesian water and sparkling drink.<br /><br />Terry cloth robe, slippers<br /><br />Handmade Amish wildflower soap<br /><br />Candy bowl<br /><br />Trail mix jar<br /><br />CDC Cleaning
2nd rowAn oasis of tranquility awaits you.
3rd rowCentrally located.... Furnished with King Size Bed etc., Desk, Spectrum 200 mbps WIFI, 4.6 cu ft. refridgerator, Microwave,. There is a large counter space to do some basic food prep but no heavy cooking. For example: Preparation of salad, sandwiches, protein shakes, warm up in microwave meals, etc. Extra Large bathroom with Jacuzzi and large shower, Brazilian cherry hardwood floors, Thomasville bedroom furniture, huge walk in closet etc.
4th rowOur distinctive bachelor's studio for 1-3 guests<br />features a pebbled bathroom,<br />strand bamboo flooring, a fireplace<br /> reaching up to vaulted, beamed ceiling<br /> and steps up to a glimmering pool!<br />You can dine outdoors under an umbrella<br />and grill up your favorite delights! <br /> This is a private room attached<br /> to main home with a backyard entrance.<br />The pool may be shared with others.
5th rowRun Runyon Canyon & Views<br /><br />Gym & Sauna<br /><br />Beautifully Furnished Room, Wrap-Around Terrace, Spa-Appointed, Runyon Canyon Views, Dressing Room/Walk-In Closet<br /><br />Upscale: High Ceilings High Doors Wide Halls Building<br /><br />First Morning: complimentary Starbucks coffee, latte-style, protein bar, granola bars, muffin or bagel, & continental breakfast upon request. <br /><br />Bottles of artesian water or similar<br /><br />Terrycloth robe & slippers<br /> <br />Handmade wildflower & other speciality soaps<br /><br />Weekly candy bowl<br /><br />Trail mix jar.
ValueCountFrequency (%)
and 110888
 
4.0%
the 99554
 
3.6%
a 78741
 
2.9%
to 68183
 
2.5%
in 53106
 
1.9%
with 44500
 
1.6%
of 42210
 
1.5%
br 39977
 
1.4%
36756
 
1.3%
is 34368
 
1.2%
Other values (46798) 2152327
78.0%
2025-08-31T11:06:33.082237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2744964
16.1%
e 1440540
 
8.5%
a 1065846
 
6.3%
o 1052396
 
6.2%
t 1012912
 
6.0%
r 898592
 
5.3%
i 898220
 
5.3%
n 881369
 
5.2%
s 769294
 
4.5%
l 597553
 
3.5%
Other values (1536) 5643306
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17004992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2744964
16.1%
e 1440540
 
8.5%
a 1065846
 
6.3%
o 1052396
 
6.2%
t 1012912
 
6.0%
r 898592
 
5.3%
i 898220
 
5.3%
n 881369
 
5.2%
s 769294
 
4.5%
l 597553
 
3.5%
Other values (1536) 5643306
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17004992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2744964
16.1%
e 1440540
 
8.5%
a 1065846
 
6.3%
o 1052396
 
6.2%
t 1012912
 
6.0%
r 898592
 
5.3%
i 898220
 
5.3%
n 881369
 
5.2%
s 769294
 
4.5%
l 597553
 
3.5%
Other values (1536) 5643306
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17004992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2744964
16.1%
e 1440540
 
8.5%
a 1065846
 
6.3%
o 1052396
 
6.2%
t 1012912
 
6.0%
r 898592
 
5.3%
i 898220
 
5.3%
n 881369
 
5.2%
s 769294
 
4.5%
l 597553
 
3.5%
Other values (1536) 5643306
33.2%

neighborhood_overview
Text

Missing 

Distinct16967
Distinct (%)83.4%
Missing25071
Missing (%)55.2%
Memory size355.0 KiB
2025-08-31T11:06:33.553010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1000
Median length797
Mean length377.4287469
Min length1

Characters and Unicode

Total characters7680675
Distinct characters815
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15623 ?
Unique (%)76.8%

Sample

1st rowWalk and run to Runyon Canyon, it is open!<br /><br />We are minutes away from the Mentor Language Institute, Kings College, Musicians Institute, and many film schools including AFI, and the American Academy of Dramatic Arts. <br />Halfway between UCLA and USC. <br /><br />We are minutes away from the Hollywood Boulevard Walk of Fame and all the clubs on Sunset Strip. All the comedy clubs are here, as well.<br /><br />Minutes from the Grove and Rodeo Drive. I'll give you maps and directions to everything. <br />Universal City is just up the road. Magic Mountain is a short drive out of town. Disneyland, as well.
2nd rowThis is the best part of Santa Monica. Quiet, calm, safe.
3rd rowWhat makes the neighborhood unique is that there are 5 grocery stores within 5 minutes and 2 Malls within 7 minutes. There are also many parks and with the San Gabriel Pass being a few minutes away, you can actually ride a bike to Seal Beach. The 91 freeway is 2 minutes away and the 605 3 minutes. The 105 freeway about 6 minutes and the 5 freeway about 6 minutes. The closest beach is about 12 minutes away. Downtown LA is about 20 minutes. Disneyland is about 12 minutes.
4th rowWe are in the middle of one of the great cities of the world and the entertainment capital of the world. I am happy to share all that I know.
5th rowI love this area because it's safe and convenient to everything because it's right in the middle of LA.<br /><br />I'm 1.5 miles to LACMA, The Academy Museum and The Tar Pits, as well as, Rodeo Drive. <br /><br />An easy walk (4-10 blocks) along Robertson Blvd. to that shopping district, The Ivy, Kitson and WeHo interiors shopping. 1 mile from Cedars-Sinai and the Beverly Center. It's 2 miles to The Grove/Farmers Market and 2 miles to the center of West Hollywood. <br /><br />I'm a 10 minute drive to UCLA or Culver City, a 20 minute drive to the ocean/Santa Monica or to the heart of Hollywood, and a 25 minute drive from LAX or to Downtown.
ValueCountFrequency (%)
the 56754
 
4.5%
and 50228
 
4.0%
to 32244
 
2.6%
a 29079
 
2.3%
of 27334
 
2.2%
is 23898
 
1.9%
br 22864
 
1.8%
20790
 
1.7%
in 18040
 
1.4%
from 10464
 
0.8%
Other values (27915) 962508
76.7%
2025-08-31T11:06:34.421382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1253770
16.3%
e 635257
 
8.3%
a 497683
 
6.5%
o 456684
 
5.9%
t 448051
 
5.8%
n 410164
 
5.3%
r 409949
 
5.3%
i 407397
 
5.3%
s 378223
 
4.9%
l 273652
 
3.6%
Other values (805) 2509845
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7680675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1253770
16.3%
e 635257
 
8.3%
a 497683
 
6.5%
o 456684
 
5.9%
t 448051
 
5.8%
n 410164
 
5.3%
r 409949
 
5.3%
i 407397
 
5.3%
s 378223
 
4.9%
l 273652
 
3.6%
Other values (805) 2509845
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7680675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1253770
16.3%
e 635257
 
8.3%
a 497683
 
6.5%
o 456684
 
5.9%
t 448051
 
5.8%
n 410164
 
5.3%
r 409949
 
5.3%
i 407397
 
5.3%
s 378223
 
4.9%
l 273652
 
3.6%
Other values (805) 2509845
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7680675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1253770
16.3%
e 635257
 
8.3%
a 497683
 
6.5%
o 456684
 
5.9%
t 448051
 
5.8%
n 410164
 
5.3%
r 409949
 
5.3%
i 407397
 
5.3%
s 378223
 
4.9%
l 273652
 
3.6%
Other values (805) 2509845
32.7%
Distinct44247
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:34.795051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length156
Median length152
Mean length104.4989762
Min length60

Characters and Unicode

Total characters4746448
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43485 ?
Unique (%)95.7%

Sample

1st rowhttps://a0.muscache.com/pictures/hosting/Hosting-U3RheVN1cHBseUxpc3Rpbmc6MjcwOA==/original/24013fea-aeb2-4213-b98e-330bc05a832d.jpeg
2nd rowhttps://a0.muscache.com/pictures/1082993/c5a9956f_original.jpg
3rd rowhttps://a0.muscache.com/pictures/23817858/de20cdd9_original.jpg
4th rowhttps://a0.muscache.com/pictures/458111/986c76ee_original.jpg
5th rowhttps://a0.muscache.com/pictures/miso/Hosting-6931/original/0c3b0dfe-c8f8-45be-9706-42244bf98380.jpeg
ValueCountFrequency (%)
https://a0.muscache.com/pictures/miso/hosting-1377249014772523867/original/ab4acae8-5251-4a62-9df3-932271caa69f.jpeg 25
 
0.1%
https://a0.muscache.com/pictures/miso/hosting-1269736507120058187/original/c14c6da1-d59f-4877-ad4c-17c319de44c0.png 14
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-1293656838440542404/original/7f152bc8-c68c-4b6a-a009-8f49fa5d082c.jpeg 14
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-1315499925115056609/original/7a101e92-ddb7-41ab-a042-67007b3f69e3.jpeg 13
 
< 0.1%
https://a0.muscache.com/pictures/e616305f-946f-4f8c-a27f-978f7d6cdb91.jpg 11
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-1370409404319016365/original/88384aaa-cecc-49ea-90ac-3c871a83be26.jpeg 11
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-1304608140502966338/original/01bb0741-265e-4ccf-95b8-074fcae1634e.jpeg 9
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-1221161121199838023/original/a270eb1e-88f2-4fe4-9f91-70f8b7f418d8.jpeg 9
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-1242841727425729823/original/8aaa2be1-4548-4392-b044-425b155fa820.jpeg 9
 
< 0.1%
https://a0.muscache.com/pictures/f6e53287-3bb6-4269-91c9-d852bc42b9be.jpg 9
 
< 0.1%
Other values (44237) 45297
99.7%
2025-08-31T11:06:35.363911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 279752
 
5.9%
/ 274712
 
5.8%
a 222151
 
4.7%
e 211178
 
4.4%
- 210726
 
4.4%
s 200919
 
4.2%
t 181316
 
3.8%
4 174223
 
3.7%
0 173417
 
3.7%
i 170114
 
3.6%
Other values (52) 2647940
55.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 279752
 
5.9%
/ 274712
 
5.8%
a 222151
 
4.7%
e 211178
 
4.4%
- 210726
 
4.4%
s 200919
 
4.2%
t 181316
 
3.8%
4 174223
 
3.7%
0 173417
 
3.7%
i 170114
 
3.6%
Other values (52) 2647940
55.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 279752
 
5.9%
/ 274712
 
5.8%
a 222151
 
4.7%
e 211178
 
4.4%
- 210726
 
4.4%
s 200919
 
4.2%
t 181316
 
3.8%
4 174223
 
3.7%
0 173417
 
3.7%
i 170114
 
3.6%
Other values (52) 2647940
55.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 279752
 
5.9%
/ 274712
 
5.8%
a 222151
 
4.7%
e 211178
 
4.4%
- 210726
 
4.4%
s 200919
 
4.2%
t 181316
 
3.8%
4 174223
 
3.7%
0 173417
 
3.7%
i 170114
 
3.6%
Other values (52) 2647940
55.8%

host_id
Real number (ℝ)

Distinct23004
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213770804
Minimum767
Maximum701545457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:35.557719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum767
5-th percentile2766433
Q128483717
median124925839
Q3404254876
95-th percentile596606455
Maximum701545457
Range701544690
Interquartile range (IQR)375771159

Descriptive statistics

Standard deviation210793650
Coefficient of variation (CV)0.9860731497
Kurtosis-0.8296518864
Mean213770804
Median Absolute Deviation (MAD)115104639
Skewness0.7384951974
Sum9.709683688 × 1012
Variance4.443396289 × 1016
MonotonicityNot monotonic
2025-08-31T11:06:35.770176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107434423 599
 
1.3%
668954332 152
 
0.3%
566639401 146
 
0.3%
501999278 138
 
0.3%
446820235 135
 
0.3%
30850484 135
 
0.3%
271118401 125
 
0.3%
468914943 123
 
0.3%
126644161 123
 
0.3%
656861752 113
 
0.2%
Other values (22994) 43632
96.1%
ValueCountFrequency (%)
767 1
< 0.1%
2410 1
< 0.1%
3008 2
< 0.1%
3041 2
< 0.1%
3144 1
< 0.1%
ValueCountFrequency (%)
701545457 3
< 0.1%
701324478 1
 
< 0.1%
701308195 2
< 0.1%
701283773 1
 
< 0.1%
701193919 1
 
< 0.1%
Distinct23004
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:36.141037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length43
Mean length42.39406882
Min length37

Characters and Unicode

Total characters1925581
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17182 ?
Unique (%)37.8%

Sample

1st rowhttps://www.airbnb.com/users/show/3008
2nd rowhttps://www.airbnb.com/users/show/3041
3rd rowhttps://www.airbnb.com/users/show/3207
4th rowhttps://www.airbnb.com/users/show/11619
5th rowhttps://www.airbnb.com/users/show/3008
ValueCountFrequency (%)
https://www.airbnb.com/users/show/107434423 599
 
1.3%
https://www.airbnb.com/users/show/668954332 152
 
0.3%
https://www.airbnb.com/users/show/566639401 146
 
0.3%
https://www.airbnb.com/users/show/501999278 138
 
0.3%
https://www.airbnb.com/users/show/446820235 135
 
0.3%
https://www.airbnb.com/users/show/30850484 135
 
0.3%
https://www.airbnb.com/users/show/271118401 125
 
0.3%
https://www.airbnb.com/users/show/468914943 123
 
0.3%
https://www.airbnb.com/users/show/126644161 123
 
0.3%
https://www.airbnb.com/users/show/656861752 113
 
0.2%
Other values (22994) 43632
96.1%
2025-08-31T11:06:36.701188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 227105
 
11.8%
s 181684
 
9.4%
w 181684
 
9.4%
h 90842
 
4.7%
r 90842
 
4.7%
t 90842
 
4.7%
b 90842
 
4.7%
o 90842
 
4.7%
. 90842
 
4.7%
a 45421
 
2.4%
Other values (18) 744635
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1925581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 227105
 
11.8%
s 181684
 
9.4%
w 181684
 
9.4%
h 90842
 
4.7%
r 90842
 
4.7%
t 90842
 
4.7%
b 90842
 
4.7%
o 90842
 
4.7%
. 90842
 
4.7%
a 45421
 
2.4%
Other values (18) 744635
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1925581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 227105
 
11.8%
s 181684
 
9.4%
w 181684
 
9.4%
h 90842
 
4.7%
r 90842
 
4.7%
t 90842
 
4.7%
b 90842
 
4.7%
o 90842
 
4.7%
. 90842
 
4.7%
a 45421
 
2.4%
Other values (18) 744635
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1925581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 227105
 
11.8%
s 181684
 
9.4%
w 181684
 
9.4%
h 90842
 
4.7%
r 90842
 
4.7%
t 90842
 
4.7%
b 90842
 
4.7%
o 90842
 
4.7%
. 90842
 
4.7%
a 45421
 
2.4%
Other values (18) 744635
38.7%
Distinct8831
Distinct (%)19.4%
Missing5
Missing (%)< 0.1%
Memory size355.0 KiB
2025-08-31T11:06:37.133289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length33
Mean length6.58670953
Min length1

Characters and Unicode

Total characters299142
Distinct characters192
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4692 ?
Unique (%)10.3%

Sample

1st rowChas.
2nd rowLouise-Diane
3rd rowBernadine
4th rowSarah
5th rowChas.
ValueCountFrequency (%)
blueground 599
 
1.1%
and 546
 
1.0%
463
 
0.9%
david 451
 
0.9%
michael 394
 
0.7%
roompicks 369
 
0.7%
john 253
 
0.5%
jason 247
 
0.5%
the 234
 
0.4%
daniel 233
 
0.4%
Other values (7987) 49082
92.8%
2025-08-31T11:06:37.787840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 34959
 
11.7%
e 27654
 
9.2%
n 25502
 
8.5%
i 23714
 
7.9%
r 16365
 
5.5%
l 14521
 
4.9%
o 14197
 
4.7%
s 9704
 
3.2%
t 8823
 
2.9%
h 8807
 
2.9%
Other values (182) 114896
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 34959
 
11.7%
e 27654
 
9.2%
n 25502
 
8.5%
i 23714
 
7.9%
r 16365
 
5.5%
l 14521
 
4.9%
o 14197
 
4.7%
s 9704
 
3.2%
t 8823
 
2.9%
h 8807
 
2.9%
Other values (182) 114896
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 34959
 
11.7%
e 27654
 
9.2%
n 25502
 
8.5%
i 23714
 
7.9%
r 16365
 
5.5%
l 14521
 
4.9%
o 14197
 
4.7%
s 9704
 
3.2%
t 8823
 
2.9%
h 8807
 
2.9%
Other values (182) 114896
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 34959
 
11.7%
e 27654
 
9.2%
n 25502
 
8.5%
i 23714
 
7.9%
r 16365
 
5.5%
l 14521
 
4.9%
o 14197
 
4.7%
s 9704
 
3.2%
t 8823
 
2.9%
h 8807
 
2.9%
Other values (182) 114896
38.4%
Distinct5234
Distinct (%)11.5%
Missing7
Missing (%)< 0.1%
Memory size355.0 KiB
2025-08-31T11:06:38.123280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters454140
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique556 ?
Unique (%)1.2%

Sample

1st row2008-09-16
2nd row2008-09-17
3rd row2008-09-25
4th row2009-03-28
5th row2008-09-16
ValueCountFrequency (%)
2016-12-16 606
 
1.3%
2023-02-20 203
 
0.4%
2024-03-12 158
 
0.3%
2024-12-26 153
 
0.3%
2015-04-08 152
 
0.3%
2017-04-19 141
 
0.3%
2022-02-25 140
 
0.3%
2019-06-24 131
 
0.3%
2016-05-19 129
 
0.3%
2022-07-11 124
 
0.3%
Other values (5224) 43477
95.7%
2025-08-31T11:06:38.611115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 103837
22.9%
- 90828
20.0%
2 90564
19.9%
1 73966
16.3%
3 16170
 
3.6%
6 15158
 
3.3%
4 14795
 
3.3%
5 14145
 
3.1%
7 12262
 
2.7%
9 11460
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 454140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 103837
22.9%
- 90828
20.0%
2 90564
19.9%
1 73966
16.3%
3 16170
 
3.6%
6 15158
 
3.3%
4 14795
 
3.3%
5 14145
 
3.1%
7 12262
 
2.7%
9 11460
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 454140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 103837
22.9%
- 90828
20.0%
2 90564
19.9%
1 73966
16.3%
3 16170
 
3.6%
6 15158
 
3.3%
4 14795
 
3.3%
5 14145
 
3.1%
7 12262
 
2.7%
9 11460
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 454140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 103837
22.9%
- 90828
20.0%
2 90564
19.9%
1 73966
16.3%
3 16170
 
3.6%
6 15158
 
3.3%
4 14795
 
3.3%
5 14145
 
3.1%
7 12262
 
2.7%
9 11460
 
2.5%

host_location
Text

Missing 

Distinct845
Distinct (%)2.4%
Missing10781
Missing (%)23.7%
Memory size355.0 KiB
2025-08-31T11:06:39.046069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length15
Mean length14.92984988
Min length4

Characters and Unicode

Total characters517170
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique421 ?
Unique (%)1.2%

Sample

1st rowLos Angeles, CA
2nd rowSanta Monica, CA
3rd rowBellflower, CA
4th rowCalifornia, United States
5th rowLos Angeles, CA
ValueCountFrequency (%)
ca 30889
31.5%
los 19086
19.5%
angeles 19075
19.4%
beach 1915
 
2.0%
united 1242
 
1.3%
states 1077
 
1.1%
santa 1071
 
1.1%
long 965
 
1.0%
california 937
 
1.0%
new 881
 
0.9%
Other values (953) 20970
21.4%
2025-08-31T11:06:39.669814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63468
12.3%
e 53428
10.3%
A 51472
10.0%
s 43905
 
8.5%
, 34441
 
6.7%
C 33630
 
6.5%
n 32222
 
6.2%
o 29977
 
5.8%
l 28179
 
5.4%
g 21677
 
4.2%
Other values (56) 124771
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 517170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
63468
12.3%
e 53428
10.3%
A 51472
10.0%
s 43905
 
8.5%
, 34441
 
6.7%
C 33630
 
6.5%
n 32222
 
6.2%
o 29977
 
5.8%
l 28179
 
5.4%
g 21677
 
4.2%
Other values (56) 124771
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 517170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
63468
12.3%
e 53428
10.3%
A 51472
10.0%
s 43905
 
8.5%
, 34441
 
6.7%
C 33630
 
6.5%
n 32222
 
6.2%
o 29977
 
5.8%
l 28179
 
5.4%
g 21677
 
4.2%
Other values (56) 124771
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 517170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
63468
12.3%
e 53428
10.3%
A 51472
10.0%
s 43905
 
8.5%
, 34441
 
6.7%
C 33630
 
6.5%
n 32222
 
6.2%
o 29977
 
5.8%
l 28179
 
5.4%
g 21677
 
4.2%
Other values (56) 124771
24.1%

host_about
Text

Missing 

Distinct11646
Distinct (%)46.8%
Missing20546
Missing (%)45.2%
Memory size355.0 KiB
2025-08-31T11:06:40.072929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5526
Median length1063
Mean length321.6246834
Min length1

Characters and Unicode

Total characters8000414
Distinct characters1588
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8342 ?
Unique (%)33.5%

Sample

1st rowProfessional and technical writer. Literary Consultant. Entertainment Industry Consultant. I wear many hats. Professional. Pleasant. Respectful. Optimistic and cheerful.
2nd rowI have been teaching yoga and meditation for 30 years. World-traveled,passionate,love life and committed to making the world a healthier place one person and one company at a time. Enjoy meeting new and interesting people.
3rd rowFair, open, honest and very informative for new guests to the area. Former Real Estate Broker who loves to talk about Real Estate, Economy, Finance, Credit, Trading, etc. I drive Uber in my spare time to get out and see the beauty of Los Angeles and Orange County. I love opening my home to people all over the world.
4th rowMy name is Sarah working in property design and development. I enjoy a quiet, coastal lifestyle and am interested in architecture, fine art, real estate and construction. Living in California, I am fortunate to have great opportunities to enjoy nature and life a healthy life. I gravitate towards calm, relaxed, upbeat guests. If you have a concern or interest during your stay, please contact me and I am happy to help you.
5th rowProfessional and technical writer. Literary Consultant. Entertainment Industry Consultant. I wear many hats. Professional. Pleasant. Respectful. Optimistic and cheerful.
ValueCountFrequency (%)
and 65030
 
4.7%
to 43775
 
3.2%
a 37908
 
2.8%
i 36113
 
2.6%
the 35453
 
2.6%
in 27477
 
2.0%
of 20053
 
1.5%
my 19366
 
1.4%
you 17974
 
1.3%
with 16731
 
1.2%
Other values (21361) 1054332
76.7%
2025-08-31T11:06:40.664110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1358375
17.0%
e 763039
 
9.5%
a 517067
 
6.5%
o 501381
 
6.3%
t 463799
 
5.8%
n 455367
 
5.7%
i 413798
 
5.2%
r 396354
 
5.0%
s 362025
 
4.5%
l 291651
 
3.6%
Other values (1578) 2477558
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1358375
17.0%
e 763039
 
9.5%
a 517067
 
6.5%
o 501381
 
6.3%
t 463799
 
5.8%
n 455367
 
5.7%
i 413798
 
5.2%
r 396354
 
5.0%
s 362025
 
4.5%
l 291651
 
3.6%
Other values (1578) 2477558
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1358375
17.0%
e 763039
 
9.5%
a 517067
 
6.5%
o 501381
 
6.3%
t 463799
 
5.8%
n 455367
 
5.7%
i 413798
 
5.2%
r 396354
 
5.0%
s 362025
 
4.5%
l 291651
 
3.6%
Other values (1578) 2477558
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1358375
17.0%
e 763039
 
9.5%
a 517067
 
6.5%
o 501381
 
6.3%
t 463799
 
5.8%
n 455367
 
5.7%
i 413798
 
5.2%
r 396354
 
5.0%
s 362025
 
4.5%
l 291651
 
3.6%
Other values (1578) 2477558
31.0%

host_response_time
Text

Missing 

Distinct4
Distinct (%)< 0.1%
Missing10393
Missing (%)22.9%
Memory size355.0 KiB
2025-08-31T11:06:40.848903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length14.51318945
Min length12

Characters and Unicode

Total characters508368
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin an hour
2nd rowwithin a few hours
3rd rowwithin a few hours
4th rowwithin an hour
5th rowwithin an hour
ValueCountFrequency (%)
within 33949
30.4%
an 27138
24.3%
hour 27138
24.3%
a 7890
 
7.1%
few 5626
 
5.0%
hours 4547
 
4.1%
day 2264
 
2.0%
days 1079
 
1.0%
or 1079
 
1.0%
more 1079
 
1.0%
2025-08-31T11:06:41.189504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
76761
15.1%
i 67898
13.4%
h 65634
12.9%
n 61087
12.0%
w 39575
7.8%
a 38371
7.5%
t 33949
6.7%
o 33843
6.7%
r 33843
6.7%
u 31685
6.2%
Other values (6) 25722
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 508368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
76761
15.1%
i 67898
13.4%
h 65634
12.9%
n 61087
12.0%
w 39575
7.8%
a 38371
7.5%
t 33949
6.7%
o 33843
6.7%
r 33843
6.7%
u 31685
6.2%
Other values (6) 25722
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 508368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
76761
15.1%
i 67898
13.4%
h 65634
12.9%
n 61087
12.0%
w 39575
7.8%
a 38371
7.5%
t 33949
6.7%
o 33843
6.7%
r 33843
6.7%
u 31685
6.2%
Other values (6) 25722
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 508368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
76761
15.1%
i 67898
13.4%
h 65634
12.9%
n 61087
12.0%
w 39575
7.8%
a 38371
7.5%
t 33949
6.7%
o 33843
6.7%
r 33843
6.7%
u 31685
6.2%
Other values (6) 25722
 
5.1%

host_response_rate
Text

Missing 

Distinct72
Distinct (%)0.2%
Missing10393
Missing (%)22.9%
Memory size355.0 KiB
2025-08-31T11:06:41.403335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.747516273
Min length2

Characters and Unicode

Total characters131268
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row100%
2nd row100%
3rd row100%
4th row100%
5th row100%
ValueCountFrequency (%)
100 26905
76.8%
99 1564
 
4.5%
90 712
 
2.0%
0 694
 
2.0%
97 504
 
1.4%
96 499
 
1.4%
98 477
 
1.4%
95 339
 
1.0%
92 289
 
0.8%
80 251
 
0.7%
Other values (62) 2794
 
8.0%
2025-08-31T11:06:41.815161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 56063
42.7%
% 35028
26.7%
1 27239
20.8%
9 6650
 
5.1%
8 1812
 
1.4%
7 1197
 
0.9%
6 965
 
0.7%
5 846
 
0.6%
3 565
 
0.4%
2 534
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 56063
42.7%
% 35028
26.7%
1 27239
20.8%
9 6650
 
5.1%
8 1812
 
1.4%
7 1197
 
0.9%
6 965
 
0.7%
5 846
 
0.6%
3 565
 
0.4%
2 534
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 56063
42.7%
% 35028
26.7%
1 27239
20.8%
9 6650
 
5.1%
8 1812
 
1.4%
7 1197
 
0.9%
6 965
 
0.7%
5 846
 
0.6%
3 565
 
0.4%
2 534
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 56063
42.7%
% 35028
26.7%
1 27239
20.8%
9 6650
 
5.1%
8 1812
 
1.4%
7 1197
 
0.9%
6 965
 
0.7%
5 846
 
0.6%
3 565
 
0.4%
2 534
 
0.4%

host_acceptance_rate
Text

Missing 

Distinct101
Distinct (%)0.3%
Missing9781
Missing (%)21.5%
Memory size355.0 KiB
2025-08-31T11:06:42.088786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.276122334
Min length2

Characters and Unicode

Total characters116761
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row100%
2nd row50%
3rd row80%
4th row100%
5th row98%
ValueCountFrequency (%)
100 11097
31.1%
99 3015
 
8.5%
98 2182
 
6.1%
97 1866
 
5.2%
96 1286
 
3.6%
0 1203
 
3.4%
94 808
 
2.3%
95 798
 
2.2%
88 747
 
2.1%
93 686
 
1.9%
Other values (91) 11952
33.5%
2025-08-31T11:06:42.773630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 35640
30.5%
0 25597
21.9%
9 16379
14.0%
1 12486
 
10.7%
8 7693
 
6.6%
7 5356
 
4.6%
6 4100
 
3.5%
5 3057
 
2.6%
4 2557
 
2.2%
3 2214
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 116761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 35640
30.5%
0 25597
21.9%
9 16379
14.0%
1 12486
 
10.7%
8 7693
 
6.6%
7 5356
 
4.6%
6 4100
 
3.5%
5 3057
 
2.6%
4 2557
 
2.2%
3 2214
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 116761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 35640
30.5%
0 25597
21.9%
9 16379
14.0%
1 12486
 
10.7%
8 7693
 
6.6%
7 5356
 
4.6%
6 4100
 
3.5%
5 3057
 
2.6%
4 2557
 
2.2%
3 2214
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 116761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 35640
30.5%
0 25597
21.9%
9 16379
14.0%
1 12486
 
10.7%
8 7693
 
6.6%
7 5356
 
4.6%
6 4100
 
3.5%
5 3057
 
2.6%
4 2557
 
2.2%
3 2214
 
1.9%

host_is_superhost
Text

Missing 

Distinct2
Distinct (%)< 0.1%
Missing1569
Missing (%)3.5%
Memory size355.0 KiB
2025-08-31T11:06:42.912672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43852
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowf
3rd rowf
4th rowf
5th rowt
ValueCountFrequency (%)
f 26901
61.3%
t 16951
38.7%
2025-08-31T11:06:43.180804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 26901
61.3%
t 16951
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 26901
61.3%
t 16951
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 26901
61.3%
t 16951
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 26901
61.3%
t 16951
38.7%
Distinct22100
Distinct (%)48.7%
Missing7
Missing (%)< 0.1%
Memory size355.0 KiB
2025-08-31T11:06:43.439196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length131
Median length106
Mean length110.4369798
Min length55

Characters and Unicode

Total characters5015385
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16426 ?
Unique (%)36.2%

Sample

1st rowhttps://a0.muscache.com/im/pictures/user/d17cfddd-9f98-4d0c-bfee-c005cc38a7de.jpg?aki_policy=profile_small
2nd rowhttps://a0.muscache.com/im/users/3041/profile_pic/1331080494/original.jpg?aki_policy=profile_small
3rd rowhttps://a0.muscache.com/im/pictures/user/8b82a267-bc4b-4d8b-935a-463a39c8c5ae.jpg?aki_policy=profile_small
4th rowhttps://a0.muscache.com/im/pictures/user/d612b34a-f2e8-4f8e-b47e-9e978c584037.jpg?aki_policy=profile_small
5th rowhttps://a0.muscache.com/im/pictures/user/d17cfddd-9f98-4d0c-bfee-c005cc38a7de.jpg?aki_policy=profile_small
ValueCountFrequency (%)
https://a0.muscache.com/defaults/user_pic-50x50.png?v=3 1295
 
2.9%
https://a0.muscache.com/im/pictures/user/d0ad9599-6fc0-4be6-865e-ffe99142517c.jpg?aki_policy=profile_small 599
 
1.3%
https://a0.muscache.com/im/pictures/user/user/original/7aad3510-0d0a-4d15-86ee-7fd077acaa24.jpeg?aki_policy=profile_small 152
 
0.3%
https://a0.muscache.com/im/pictures/user/user-566639401/original/10102628-0252-4624-9420-e43cf84d6b55.png?aki_policy=profile_small 146
 
0.3%
https://a0.muscache.com/im/pictures/user/878362e2-a2de-4cea-a340-58d2bac8641e.jpg?aki_policy=profile_small 138
 
0.3%
https://a0.muscache.com/im/pictures/user/1b21ecd9-444c-4a62-8bb1-117e57b581a7.jpg?aki_policy=profile_small 135
 
0.3%
https://a0.muscache.com/im/pictures/user/user/original/ccc92c5b-f362-4d2a-b52a-f04ffc0f16a9.jpeg?aki_policy=profile_small 135
 
0.3%
https://a0.muscache.com/im/pictures/user/68a04d63-77cd-4e8f-a5fe-5132a4659b56.jpg?aki_policy=profile_small 125
 
0.3%
https://a0.muscache.com/im/pictures/user/37cc75c6-daee-48c3-b54c-019dd12333b0.jpg?aki_policy=profile_small 123
 
0.3%
https://a0.muscache.com/im/pictures/user/user-126644161/original/6bfed478-c2a3-441b-9859-bf604dad810a.png?aki_policy=profile_small 123
 
0.3%
Other values (22090) 42443
93.5%
2025-08-31T11:06:43.898022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 306834
 
6.1%
c 302327
 
6.0%
e 283927
 
5.7%
a 283381
 
5.7%
i 262836
 
5.2%
s 240239
 
4.8%
p 228486
 
4.6%
l 200247
 
4.0%
m 178978
 
3.6%
- 169082
 
3.4%
Other values (32) 2559048
51.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5015385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 306834
 
6.1%
c 302327
 
6.0%
e 283927
 
5.7%
a 283381
 
5.7%
i 262836
 
5.2%
s 240239
 
4.8%
p 228486
 
4.6%
l 200247
 
4.0%
m 178978
 
3.6%
- 169082
 
3.4%
Other values (32) 2559048
51.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5015385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 306834
 
6.1%
c 302327
 
6.0%
e 283927
 
5.7%
a 283381
 
5.7%
i 262836
 
5.2%
s 240239
 
4.8%
p 228486
 
4.6%
l 200247
 
4.0%
m 178978
 
3.6%
- 169082
 
3.4%
Other values (32) 2559048
51.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5015385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 306834
 
6.1%
c 302327
 
6.0%
e 283927
 
5.7%
a 283381
 
5.7%
i 262836
 
5.2%
s 240239
 
4.8%
p 228486
 
4.6%
l 200247
 
4.0%
m 178978
 
3.6%
- 169082
 
3.4%
Other values (32) 2559048
51.0%
Distinct22100
Distinct (%)48.7%
Missing7
Missing (%)< 0.1%
Memory size355.0 KiB
2025-08-31T11:06:44.159654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length134
Median length109
Mean length113.4084644
Min length57

Characters and Unicode

Total characters5150332
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16426 ?
Unique (%)36.2%

Sample

1st rowhttps://a0.muscache.com/im/pictures/user/d17cfddd-9f98-4d0c-bfee-c005cc38a7de.jpg?aki_policy=profile_x_medium
2nd rowhttps://a0.muscache.com/im/users/3041/profile_pic/1331080494/original.jpg?aki_policy=profile_x_medium
3rd rowhttps://a0.muscache.com/im/pictures/user/8b82a267-bc4b-4d8b-935a-463a39c8c5ae.jpg?aki_policy=profile_x_medium
4th rowhttps://a0.muscache.com/im/pictures/user/d612b34a-f2e8-4f8e-b47e-9e978c584037.jpg?aki_policy=profile_x_medium
5th rowhttps://a0.muscache.com/im/pictures/user/d17cfddd-9f98-4d0c-bfee-c005cc38a7de.jpg?aki_policy=profile_x_medium
ValueCountFrequency (%)
https://a0.muscache.com/defaults/user_pic-225x225.png?v=3 1295
 
2.9%
https://a0.muscache.com/im/pictures/user/d0ad9599-6fc0-4be6-865e-ffe99142517c.jpg?aki_policy=profile_x_medium 599
 
1.3%
https://a0.muscache.com/im/pictures/user/user/original/7aad3510-0d0a-4d15-86ee-7fd077acaa24.jpeg?aki_policy=profile_x_medium 152
 
0.3%
https://a0.muscache.com/im/pictures/user/user-566639401/original/10102628-0252-4624-9420-e43cf84d6b55.png?aki_policy=profile_x_medium 146
 
0.3%
https://a0.muscache.com/im/pictures/user/878362e2-a2de-4cea-a340-58d2bac8641e.jpg?aki_policy=profile_x_medium 138
 
0.3%
https://a0.muscache.com/im/pictures/user/1b21ecd9-444c-4a62-8bb1-117e57b581a7.jpg?aki_policy=profile_x_medium 135
 
0.3%
https://a0.muscache.com/im/pictures/user/user/original/ccc92c5b-f362-4d2a-b52a-f04ffc0f16a9.jpeg?aki_policy=profile_x_medium 135
 
0.3%
https://a0.muscache.com/im/pictures/user/68a04d63-77cd-4e8f-a5fe-5132a4659b56.jpg?aki_policy=profile_x_medium 125
 
0.3%
https://a0.muscache.com/im/pictures/user/37cc75c6-daee-48c3-b54c-019dd12333b0.jpg?aki_policy=profile_x_medium 123
 
0.3%
https://a0.muscache.com/im/pictures/user/user-126644161/original/6bfed478-c2a3-441b-9859-bf604dad810a.png?aki_policy=profile_x_medium 123
 
0.3%
Other values (22090) 42443
93.5%
2025-08-31T11:06:44.617963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 328046
 
6.4%
i 306955
 
6.0%
/ 306834
 
6.0%
c 302327
 
5.9%
a 239262
 
4.6%
p 228486
 
4.4%
m 223097
 
4.3%
s 196120
 
3.8%
u 176355
 
3.4%
- 169082
 
3.3%
Other values (32) 2673768
51.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5150332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 328046
 
6.4%
i 306955
 
6.0%
/ 306834
 
6.0%
c 302327
 
5.9%
a 239262
 
4.6%
p 228486
 
4.4%
m 223097
 
4.3%
s 196120
 
3.8%
u 176355
 
3.4%
- 169082
 
3.3%
Other values (32) 2673768
51.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5150332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 328046
 
6.4%
i 306955
 
6.0%
/ 306834
 
6.0%
c 302327
 
5.9%
a 239262
 
4.6%
p 228486
 
4.4%
m 223097
 
4.3%
s 196120
 
3.8%
u 176355
 
3.4%
- 169082
 
3.3%
Other values (32) 2673768
51.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5150332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 328046
 
6.4%
i 306955
 
6.0%
/ 306834
 
6.0%
c 302327
 
5.9%
a 239262
 
4.6%
p 228486
 
4.4%
m 223097
 
4.3%
s 196120
 
3.8%
u 176355
 
3.4%
- 169082
 
3.3%
Other values (32) 2673768
51.9%

host_neighbourhood
Text

Missing 

Distinct857
Distinct (%)2.4%
Missing9507
Missing (%)20.9%
Memory size355.0 KiB
2025-08-31T11:06:44.993321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length28
Mean length11.49178593
Min length3

Characters and Unicode

Total characters412716
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique256 ?
Unique (%)0.7%

Sample

1st rowHollywood
2nd rowSanta Monica
3rd rowBellflower
4th rowWoodland Hills/Warner Center
5th rowHollywood
ValueCountFrequency (%)
la 3981
 
6.1%
central 3955
 
6.1%
los 3304
 
5.1%
angeles 3171
 
4.9%
south 2717
 
4.2%
hollywood 2445
 
3.8%
park 1645
 
2.5%
hills 1513
 
2.3%
downtown 1450
 
2.2%
venice 1387
 
2.1%
Other values (787) 39298
60.6%
2025-08-31T11:06:45.598367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 38270
 
9.3%
o 32142
 
7.8%
a 30511
 
7.4%
28952
 
7.0%
l 28623
 
6.9%
n 25319
 
6.1%
t 24268
 
5.9%
s 20645
 
5.0%
r 20641
 
5.0%
i 18881
 
4.6%
Other values (58) 144464
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 412716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 38270
 
9.3%
o 32142
 
7.8%
a 30511
 
7.4%
28952
 
7.0%
l 28623
 
6.9%
n 25319
 
6.1%
t 24268
 
5.9%
s 20645
 
5.0%
r 20641
 
5.0%
i 18881
 
4.6%
Other values (58) 144464
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 412716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 38270
 
9.3%
o 32142
 
7.8%
a 30511
 
7.4%
28952
 
7.0%
l 28623
 
6.9%
n 25319
 
6.1%
t 24268
 
5.9%
s 20645
 
5.0%
r 20641
 
5.0%
i 18881
 
4.6%
Other values (58) 144464
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 412716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 38270
 
9.3%
o 32142
 
7.8%
a 30511
 
7.4%
28952
 
7.0%
l 28623
 
6.9%
n 25319
 
6.1%
t 24268
 
5.9%
s 20645
 
5.0%
r 20641
 
5.0%
i 18881
 
4.6%
Other values (58) 144464
35.0%

host_listings_count
Real number (ℝ)

Distinct142
Distinct (%)0.3%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean120.4269829
Minimum0
Maximum4925
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:45.826101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q313
95-th percentile135
Maximum4925
Range4925
Interquartile range (IQR)12

Descriptive statistics

Standard deviation670.1541141
Coefficient of variation (CV)5.564816939
Kurtosis42.39484433
Mean120.4269829
Median Absolute Deviation (MAD)2
Skewness6.577760312
Sum5469071
Variance449106.5367
MonotonicityNot monotonic
2025-08-31T11:06:46.025772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14976
33.0%
2 5777
 
12.7%
3 3292
 
7.2%
4 2327
 
5.1%
5 1641
 
3.6%
6 1405
 
3.1%
7 1034
 
2.3%
8 1008
 
2.2%
9 742
 
1.6%
4887 599
 
1.3%
Other values (132) 12613
27.8%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 14976
33.0%
2 5777
 
12.7%
3 3292
 
7.2%
4 2327
 
5.1%
ValueCountFrequency (%)
4925 135
 
0.3%
4887 599
1.3%
4111 79
 
0.2%
3095 138
 
0.3%
2861 64
 
0.1%

host_total_listings_count
Real number (ℝ)

Distinct202
Distinct (%)0.4%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean168.2887215
Minimum0
Maximum9109
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:46.218245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q320
95-th percentile203.05
Maximum9109
Range9109
Interquartile range (IQR)18

Descriptive statistics

Standard deviation876.6745013
Coefficient of variation (CV)5.209347919
Kurtosis38.63263944
Mean168.2887215
Median Absolute Deviation (MAD)4
Skewness6.257416265
Sum7642664
Variance768558.1812
MonotonicityNot monotonic
2025-08-31T11:06:46.406962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9364
20.6%
2 5963
 
13.1%
3 3918
 
8.6%
4 2807
 
6.2%
5 2008
 
4.4%
6 1661
 
3.7%
8 1155
 
2.5%
7 1086
 
2.4%
9 984
 
2.2%
11 690
 
1.5%
Other values (192) 15778
34.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 9364
20.6%
2 5963
13.1%
3 3918
8.6%
4 2807
 
6.2%
ValueCountFrequency (%)
9109 14
 
< 0.1%
6397 138
 
0.3%
6345 135
 
0.3%
5684 599
1.3%
4794 79
 
0.2%
Distinct7
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size355.0 KiB
2025-08-31T11:06:46.558670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length18
Mean length18.67772053
Min length2

Characters and Unicode

Total characters848230
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row['email', 'phone']
2nd row['email', 'phone']
3rd row['email', 'phone']
4th row['email', 'phone']
5th row['email', 'phone']
ValueCountFrequency (%)
phone 45327
49.7%
email 40473
44.4%
work_email 5428
 
5.9%
19
 
< 0.1%
2025-08-31T11:06:46.897250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 182456
21.5%
e 91228
10.8%
o 50755
 
6.0%
m 45901
 
5.4%
a 45901
 
5.4%
i 45901
 
5.4%
l 45901
 
5.4%
, 45833
 
5.4%
45833
 
5.4%
[ 45414
 
5.4%
Other values (8) 203107
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 848230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 182456
21.5%
e 91228
10.8%
o 50755
 
6.0%
m 45901
 
5.4%
a 45901
 
5.4%
i 45901
 
5.4%
l 45901
 
5.4%
, 45833
 
5.4%
45833
 
5.4%
[ 45414
 
5.4%
Other values (8) 203107
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 848230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 182456
21.5%
e 91228
10.8%
o 50755
 
6.0%
m 45901
 
5.4%
a 45901
 
5.4%
i 45901
 
5.4%
l 45901
 
5.4%
, 45833
 
5.4%
45833
 
5.4%
[ 45414
 
5.4%
Other values (8) 203107
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 848230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 182456
21.5%
e 91228
10.8%
o 50755
 
6.0%
m 45901
 
5.4%
a 45901
 
5.4%
i 45901
 
5.4%
l 45901
 
5.4%
, 45833
 
5.4%
45833
 
5.4%
[ 45414
 
5.4%
Other values (8) 203107
23.9%
Distinct2
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size355.0 KiB
2025-08-31T11:06:47.016161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45414
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowt
3rd rowt
4th rowt
5th rowt
ValueCountFrequency (%)
t 44119
97.1%
f 1295
 
2.9%
2025-08-31T11:06:47.280954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 44119
97.1%
f 1295
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 44119
97.1%
f 1295
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 44119
97.1%
f 1295
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 44119
97.1%
f 1295
 
2.9%
Distinct2
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size355.0 KiB
2025-08-31T11:06:47.394618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45414
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowt
3rd rowt
4th rowt
5th rowt
ValueCountFrequency (%)
t 39976
88.0%
f 5438
 
12.0%
2025-08-31T11:06:47.662724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 39976
88.0%
f 5438
 
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 39976
88.0%
f 5438
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 39976
88.0%
f 5438
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 39976
88.0%
f 5438
 
12.0%

neighbourhood
Text

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing25070
Missing (%)55.2%
Memory size355.0 KiB
2025-08-31T11:06:47.835208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters468073
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeighborhood highlights
2nd rowNeighborhood highlights
3rd rowNeighborhood highlights
4th rowNeighborhood highlights
5th rowNeighborhood highlights
ValueCountFrequency (%)
neighborhood 20351
50.0%
highlights 20351
50.0%
2025-08-31T11:06:48.161609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 101755
21.7%
i 61053
13.0%
g 61053
13.0%
o 61053
13.0%
N 20351
 
4.3%
e 20351
 
4.3%
b 20351
 
4.3%
r 20351
 
4.3%
d 20351
 
4.3%
20351
 
4.3%
Other values (3) 61053
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 468073
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 101755
21.7%
i 61053
13.0%
g 61053
13.0%
o 61053
13.0%
N 20351
 
4.3%
e 20351
 
4.3%
b 20351
 
4.3%
r 20351
 
4.3%
d 20351
 
4.3%
20351
 
4.3%
Other values (3) 61053
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 468073
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 101755
21.7%
i 61053
13.0%
g 61053
13.0%
o 61053
13.0%
N 20351
 
4.3%
e 20351
 
4.3%
b 20351
 
4.3%
r 20351
 
4.3%
d 20351
 
4.3%
20351
 
4.3%
Other values (3) 61053
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 468073
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 101755
21.7%
i 61053
13.0%
g 61053
13.0%
o 61053
13.0%
N 20351
 
4.3%
e 20351
 
4.3%
b 20351
 
4.3%
r 20351
 
4.3%
d 20351
 
4.3%
20351
 
4.3%
Other values (3) 61053
13.0%
Distinct266
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:48.514226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length23
Mean length10.93641708
Min length4

Characters and Unicode

Total characters496743
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowHollywood
2nd rowSanta Monica
3rd rowBellflower
4th rowWoodland Hills
5th rowHollywood
ValueCountFrequency (%)
hollywood 5260
 
7.1%
hills 3724
 
5.0%
beach 3086
 
4.2%
west 2789
 
3.8%
park 2583
 
3.5%
beverly 1869
 
2.5%
santa 1846
 
2.5%
long 1837
 
2.5%
venice 1547
 
2.1%
heights 1487
 
2.0%
Other values (266) 47995
64.8%
2025-08-31T11:06:49.096733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 45683
 
9.2%
o 44424
 
8.9%
e 42367
 
8.5%
l 38185
 
7.7%
n 31152
 
6.3%
28602
 
5.8%
i 24289
 
4.9%
t 23873
 
4.8%
r 22333
 
4.5%
s 21092
 
4.2%
Other values (42) 174743
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 496743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 45683
 
9.2%
o 44424
 
8.9%
e 42367
 
8.5%
l 38185
 
7.7%
n 31152
 
6.3%
28602
 
5.8%
i 24289
 
4.9%
t 23873
 
4.8%
r 22333
 
4.5%
s 21092
 
4.2%
Other values (42) 174743
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 496743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 45683
 
9.2%
o 44424
 
8.9%
e 42367
 
8.5%
l 38185
 
7.7%
n 31152
 
6.3%
28602
 
5.8%
i 24289
 
4.9%
t 23873
 
4.8%
r 22333
 
4.5%
s 21092
 
4.2%
Other values (42) 174743
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 496743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 45683
 
9.2%
o 44424
 
8.9%
e 42367
 
8.5%
l 38185
 
7.7%
n 31152
 
6.3%
28602
 
5.8%
i 24289
 
4.9%
t 23873
 
4.8%
r 22333
 
4.5%
s 21092
 
4.2%
Other values (42) 174743
35.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:49.315258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length16.26538385
Min length12

Characters and Unicode

Total characters738790
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity of Los Angeles
2nd rowOther Cities
3rd rowOther Cities
4th rowCity of Los Angeles
5th rowCity of Los Angeles
ValueCountFrequency (%)
city 22774
16.7%
of 22774
16.7%
los 22774
16.7%
angeles 22774
16.7%
other 18357
13.5%
cities 18357
13.5%
unincorporated 4290
 
3.1%
areas 4290
 
3.1%
2025-08-31T11:06:49.707172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90969
12.3%
e 90842
12.3%
s 68195
 
9.2%
i 63778
 
8.6%
t 63778
 
8.6%
o 54128
 
7.3%
C 41131
 
5.6%
n 31354
 
4.2%
r 31227
 
4.2%
A 27064
 
3.7%
Other values (12) 176324
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 738790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
90969
12.3%
e 90842
12.3%
s 68195
 
9.2%
i 63778
 
8.6%
t 63778
 
8.6%
o 54128
 
7.3%
C 41131
 
5.6%
n 31354
 
4.2%
r 31227
 
4.2%
A 27064
 
3.7%
Other values (12) 176324
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 738790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
90969
12.3%
e 90842
12.3%
s 68195
 
9.2%
i 63778
 
8.6%
t 63778
 
8.6%
o 54128
 
7.3%
C 41131
 
5.6%
n 31354
 
4.2%
r 31227
 
4.2%
A 27064
 
3.7%
Other values (12) 176324
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 738790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
90969
12.3%
e 90842
12.3%
s 68195
 
9.2%
i 63778
 
8.6%
t 63778
 
8.6%
o 54128
 
7.3%
C 41131
 
5.6%
n 31354
 
4.2%
r 31227
 
4.2%
A 27064
 
3.7%
Other values (12) 176324
23.9%

latitude
Real number (ℝ)

Distinct34240
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.05527492
Minimum33.33854
Maximum34.81118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:49.934192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.33854
5-th percentile33.80315
Q133.99824
median34.06165
Q334.10838
95-th percentile34.2159
Maximum34.81118
Range1.47264
Interquartile range (IQR)0.11014

Descriptive statistics

Standard deviation0.1447762078
Coefficient of variation (CV)0.004251212423
Kurtosis6.747639681
Mean34.05527492
Median Absolute Deviation (MAD)0.05483
Skewness0.2987854607
Sum1546824.642
Variance0.02096015035
MonotonicityNot monotonic
2025-08-31T11:06:50.131837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.0438745 86
 
0.2%
34.0286879 67
 
0.1%
34.06104 46
 
0.1%
34.08984 40
 
0.1%
34.08794 39
 
0.1%
34.0244431 34
 
0.1%
34.0409275 28
 
0.1%
34.06211 25
 
0.1%
34.14556 23
 
0.1%
34.02532 22
 
< 0.1%
Other values (34230) 45011
99.1%
ValueCountFrequency (%)
33.33854 1
< 0.1%
33.3386 1
< 0.1%
33.3388 1
< 0.1%
33.33887 1
< 0.1%
33.3391539 1
< 0.1%
ValueCountFrequency (%)
34.81118 1
< 0.1%
34.81113 1
< 0.1%
34.79182 1
< 0.1%
34.77802 1
< 0.1%
34.77734 1
< 0.1%

longitude
Real number (ℝ)

Distinct36703
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-118.3099336
Minimum-118.9171338
Maximum-117.6543
Zeros0
Zeros (%)0.0%
Negative45421
Negative (%)100.0%
Memory size355.0 KiB
2025-08-31T11:06:50.594892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-118.9171338
5-th percentile-118.56488
Q1-118.41137
median-118.34064
Q3-118.2244034
95-th percentile-117.9630011
Maximum-117.6543
Range1.26283384
Interquartile range (IQR)0.1869666187

Descriptive statistics

Standard deviation0.1730133333
Coefficient of variation (CV)-0.001462373682
Kurtosis1.024641305
Mean-118.3099336
Median Absolute Deviation (MAD)0.08844
Skewness0.5667648499
Sum-5373755.495
Variance0.0299336135
MonotonicityNot monotonic
2025-08-31T11:06:50.802216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.2578334 86
 
0.2%
-118.1730148 67
 
0.1%
-118.30212 45
 
0.1%
-118.37901 43
 
0.1%
-118.38737 39
 
0.1%
-118.308239 34
 
0.1%
-118.1897 31
 
0.1%
-118.2647285 28
 
0.1%
-118.13105 23
 
0.1%
-118.30122 23
 
0.1%
Other values (36693) 45002
99.1%
ValueCountFrequency (%)
-118.9171338 1
< 0.1%
-118.9165165 1
< 0.1%
-118.90766 1
< 0.1%
-118.90728 1
< 0.1%
-118.9026893 1
< 0.1%
ValueCountFrequency (%)
-117.6543 1
< 0.1%
-117.661151 1
< 0.1%
-117.66935 1
< 0.1%
-117.68389 1
< 0.1%
-117.69779 1
< 0.1%
Distinct94
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:51.011193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length33
Mean length16.46416856
Min length3

Characters and Unicode

Total characters747819
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowPrivate room in rental unit
2nd rowPrivate room in rental unit
3rd rowEntire rental unit
4th rowEntire bungalow
5th rowPrivate room in rental unit
ValueCountFrequency (%)
entire 32676
25.1%
home 19209
14.7%
rental 14048
10.8%
unit 14048
10.8%
room 12282
 
9.4%
in 12240
 
9.4%
private 10672
 
8.2%
guesthouse 2932
 
2.2%
condo 2220
 
1.7%
guest 1368
 
1.0%
Other values (53) 8639
 
6.6%
2025-08-31T11:06:51.434416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 90182
12.1%
84913
11.4%
t 81627
10.9%
n 77875
10.4%
i 72910
9.7%
r 70051
9.4%
o 56160
7.5%
E 32682
 
4.4%
m 32045
 
4.3%
a 28941
 
3.9%
Other values (31) 120433
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 747819
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 90182
12.1%
84913
11.4%
t 81627
10.9%
n 77875
10.4%
i 72910
9.7%
r 70051
9.4%
o 56160
7.5%
E 32682
 
4.4%
m 32045
 
4.3%
a 28941
 
3.9%
Other values (31) 120433
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 747819
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 90182
12.1%
84913
11.4%
t 81627
10.9%
n 77875
10.4%
i 72910
9.7%
r 70051
9.4%
o 56160
7.5%
E 32682
 
4.4%
m 32045
 
4.3%
a 28941
 
3.9%
Other values (31) 120433
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 747819
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 90182
12.1%
84913
11.4%
t 81627
10.9%
n 77875
10.4%
i 72910
9.7%
r 70051
9.4%
o 56160
7.5%
E 32682
 
4.4%
m 32045
 
4.3%
a 28941
 
3.9%
Other values (31) 120433
16.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:51.622036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.16820413
Min length10

Characters and Unicode

Total characters643534
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowPrivate room
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowPrivate room
ValueCountFrequency (%)
entire 33192
36.5%
home/apt 33192
36.5%
room 12229
 
13.5%
private 11502
 
12.7%
hotel 367
 
0.4%
shared 360
 
0.4%
2025-08-31T11:06:51.975245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 78613
12.2%
t 78253
12.2%
o 58017
9.0%
r 57283
8.9%
m 45421
 
7.1%
45421
 
7.1%
a 45054
 
7.0%
i 44694
 
6.9%
h 33552
 
5.2%
p 33192
 
5.2%
Other values (9) 124034
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 643534
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 78613
12.2%
t 78253
12.2%
o 58017
9.0%
r 57283
8.9%
m 45421
 
7.1%
45421
 
7.1%
a 45054
 
7.0%
i 44694
 
6.9%
h 33552
 
5.2%
p 33192
 
5.2%
Other values (9) 124034
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 643534
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 78613
12.2%
t 78253
12.2%
o 58017
9.0%
r 57283
8.9%
m 45421
 
7.1%
45421
 
7.1%
a 45054
 
7.0%
i 44694
 
6.9%
h 33552
 
5.2%
p 33192
 
5.2%
Other values (9) 124034
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 643534
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 78613
12.2%
t 78253
12.2%
o 58017
9.0%
r 57283
8.9%
m 45421
 
7.1%
45421
 
7.1%
a 45054
 
7.0%
i 44694
 
6.9%
h 33552
 
5.2%
p 33192
 
5.2%
Other values (9) 124034
19.3%

accommodates
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.05046124
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:52.146633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile10
Maximum16
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.877310466
Coefficient of variation (CV)0.7103661275
Kurtosis2.739329605
Mean4.05046124
Median Absolute Deviation (MAD)1
Skewness1.563077418
Sum183976
Variance8.278915518
MonotonicityNot monotonic
2025-08-31T11:06:52.302526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 15577
34.3%
4 7992
17.6%
6 4919
 
10.8%
1 4325
 
9.5%
3 3533
 
7.8%
8 2765
 
6.1%
5 2186
 
4.8%
10 1345
 
3.0%
7 903
 
2.0%
12 618
 
1.4%
Other values (6) 1258
 
2.8%
ValueCountFrequency (%)
1 4325
 
9.5%
2 15577
34.3%
3 3533
 
7.8%
4 7992
17.6%
5 2186
 
4.8%
ValueCountFrequency (%)
16 400
0.9%
15 52
 
0.1%
14 245
 
0.5%
13 65
 
0.1%
12 618
1.4%

bathrooms
Real number (ℝ)

Missing 

Distinct34
Distinct (%)0.1%
Missing8833
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean1.653766262
Minimum0
Maximum32.5
Zeros147
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:52.474219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum32.5
Range32.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.208846129
Coefficient of variation (CV)0.7309655277
Kurtosis30.71511109
Mean1.653766262
Median Absolute Deviation (MAD)0
Skewness3.750858588
Sum60508
Variance1.461308962
MonotonicityNot monotonic
2025-08-31T11:06:52.649173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 21925
48.3%
2 6901
 
15.2%
3 1800
 
4.0%
1.5 1470
 
3.2%
2.5 1454
 
3.2%
3.5 708
 
1.6%
4 601
 
1.3%
4.5 402
 
0.9%
5 253
 
0.6%
5.5 231
 
0.5%
Other values (24) 843
 
1.9%
(Missing) 8833
19.4%
ValueCountFrequency (%)
0 147
 
0.3%
0.5 149
 
0.3%
1 21925
48.3%
1.5 1470
 
3.2%
2 6901
 
15.2%
ValueCountFrequency (%)
32.5 1
< 0.1%
24 1
< 0.1%
21 1
< 0.1%
16 1
< 0.1%
15 1
< 0.1%
Distinct53
Distinct (%)0.1%
Missing54
Missing (%)0.1%
Memory size355.0 KiB
2025-08-31T11:06:52.841990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length8.286221262
Min length6

Characters and Unicode

Total characters375921
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row1 shared bath
2nd row1 private bath
3rd row1 bath
4th row1 bath
5th row1 shared bath
ValueCountFrequency (%)
1 27917
27.8%
bath 27917
27.8%
baths 17276
17.2%
2 8256
 
8.2%
private 5065
 
5.0%
shared 4938
 
4.9%
3 2062
 
2.1%
1.5 1931
 
1.9%
2.5 1758
 
1.7%
3.5 805
 
0.8%
Other values (28) 2638
 
2.6%
2025-08-31T11:06:53.222110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 55544
14.8%
55196
14.7%
t 50432
13.4%
h 50406
13.4%
b 45367
12.1%
1 29973
8.0%
s 22166
 
5.9%
2 10025
 
2.7%
r 10003
 
2.7%
e 10003
 
2.7%
Other values (19) 36806
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 55544
14.8%
55196
14.7%
t 50432
13.4%
h 50406
13.4%
b 45367
12.1%
1 29973
8.0%
s 22166
 
5.9%
2 10025
 
2.7%
r 10003
 
2.7%
e 10003
 
2.7%
Other values (19) 36806
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 55544
14.8%
55196
14.7%
t 50432
13.4%
h 50406
13.4%
b 45367
12.1%
1 29973
8.0%
s 22166
 
5.9%
2 10025
 
2.7%
r 10003
 
2.7%
e 10003
 
2.7%
Other values (19) 36806
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 55544
14.8%
55196
14.7%
t 50432
13.4%
h 50406
13.4%
b 45367
12.1%
1 29973
8.0%
s 22166
 
5.9%
2 10025
 
2.7%
r 10003
 
2.7%
e 10003
 
2.7%
Other values (19) 36806
9.8%

bedrooms
Real number (ℝ)

Missing  Zeros 

Distinct20
Distinct (%)< 0.1%
Missing3063
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean1.80367345
Minimum0
Maximum24
Zeros2580
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:53.408232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.317981972
Coefficient of variation (CV)0.7307209473
Kurtosis8.937544162
Mean1.80367345
Median Absolute Deviation (MAD)1
Skewness1.882684347
Sum76400
Variance1.737076479
MonotonicityNot monotonic
2025-08-31T11:06:53.565820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 20706
45.6%
2 9058
19.9%
3 5460
 
12.0%
4 2785
 
6.1%
0 2580
 
5.7%
5 1126
 
2.5%
6 393
 
0.9%
7 130
 
0.3%
8 50
 
0.1%
9 30
 
0.1%
Other values (10) 40
 
0.1%
(Missing) 3063
 
6.7%
ValueCountFrequency (%)
0 2580
 
5.7%
1 20706
45.6%
2 9058
19.9%
3 5460
 
12.0%
4 2785
 
6.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
23 1
 
< 0.1%
18 1
 
< 0.1%
16 3
< 0.1%
15 1
 
< 0.1%

beds
Real number (ℝ)

Missing  Zeros 

Distinct25
Distinct (%)0.1%
Missing8876
Missing (%)19.5%
Infinite0
Infinite (%)0.0%
Mean2.25396087
Minimum0
Maximum50
Zeros554
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:53.730950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.721115047
Coefficient of variation (CV)0.7635957972
Kurtosis27.24697495
Mean2.25396087
Median Absolute Deviation (MAD)1
Skewness2.80307077
Sum82371
Variance2.962237006
MonotonicityNot monotonic
2025-08-31T11:06:53.900237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 15788
34.8%
2 8425
18.5%
3 4878
 
10.7%
4 3388
 
7.5%
5 1749
 
3.9%
6 890
 
2.0%
0 554
 
1.2%
7 384
 
0.8%
8 227
 
0.5%
9 86
 
0.2%
Other values (15) 176
 
0.4%
(Missing) 8876
19.5%
ValueCountFrequency (%)
0 554
 
1.2%
1 15788
34.8%
2 8425
18.5%
3 4878
 
10.7%
4 3388
 
7.5%
ValueCountFrequency (%)
50 1
< 0.1%
32 1
< 0.1%
25 1
< 0.1%
24 1
< 0.1%
20 2
< 0.1%
Distinct41651
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:54.185361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2697
Median length1487
Mean length666.1714405
Min length2

Characters and Unicode

Total characters30258173
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40312 ?
Unique (%)88.8%

Sample

1st row["Extra pillows and blankets", "Frigidaire gas stove", "Free street parking", "Shared patio or balcony", "Essentials", "Outdoor furniture", "Shared gym in building", "Hot water kettle", "Portable fans", "Clothing storage: closet, wardrobe, and dresser", "Paid dryer \u2013 In building", "Carbon monoxide alarm", "Central air conditioning", "Elevator", "Dishes and silverware", "Smoke alarm", "Bathtub", "Long term stays allowed", "Indoor fireplace: gas", "1801 Beekman Luxury Spa Goat Milk Soap & Handmade Hand Cut Amish Wild Flower Bars body soap", "First aid kit", "Free parking on premises", "Dishwasher", "Drying rack for clothing", "BBQ grill", "Fire extinguisher", "Oven", "Hot water", "Laundromat nearby", "Mountain view", "Hair dryer", "Exercise equipment: elliptical, free weights, stationary bike, treadmill", "Cleaning available during stay", "Outdoor dining area", "Wen, Prell, Sauve, V05, Pert, Pantene. shampoo", "Shared backyard \u2013 Fully fenced", "Fireplace guards", "Breakfast", "Cooking basics", "Samsung refrigerator", "Kitchen", "Blender", "Sun loungers", "Board games", "Freezer", "Central heating", "Baking sheet", "Books and reading material", "Luggage dropoff allowed", "Cleaning products", "Coffee maker: drip coffee maker", "Portable heater", "Barbecue utensils", "Shared sauna", "Single level home", "Wen, V05, Prell, among others. conditioner", "Lock on bedroom door", "Beach essentials", "Bed linens", "Host greets you", "Coffee", "Portable air conditioning", "Ethernet connection", "Shared hot tub - available all year, open specific hours", "Microwave", "Shower gel", "Room-darkening shades", "Iron", "Toaster", "Paid washer \u2013 In building", "Beach access \u2013 Beachfront", "Hangers", "Dining table", "Wine glasses", "Dedicated workspace", "Wifi", "Pool"]
2nd row["Host greets you", "Free street parking", "Refrigerator", "Essentials", "Washer", "Microwave", "Oven", "Hair dryer", "Heating", "Private patio or balcony", "Hangers", "Dishes and silverware", "Smoke alarm", "Dedicated workspace", "Cooking basics", "Shampoo", "Dryer", "Kitchen", "Wifi"]
3rd row["Extra pillows and blankets", "Host greets you", "Free street parking", "Refrigerator", "Essentials", "Free parking on premises", "Exterior security cameras on property", "TV", "Washer", "Air conditioning", "Microwave", "Hot water", "Hair dryer", "Heating", "Carbon monoxide alarm", "Iron", "Hangers", "Smoke alarm", "Dryer", "Bed linens", "Long term stays allowed", "Wifi"]
4th row["Gym", "Free street parking", "First aid kit", "Essentials", "Free parking on premises", "Private pool", "BBQ grill", "Pets allowed", "Washer", "Air conditioning", "Fire extinguisher", "Children\u2019s books and toys", "Backyard", "Pack \u2019n play/Travel crib", "Patio or balcony", "Hot water", "Luggage dropoff allowed", "Hair dryer", "Heating", "Cleaning available during stay", "Iron", "Hangers", "Indoor fireplace", "High chair", "TV with standard cable", "Smoke alarm", "Self check-in", "Shampoo", "Dryer", "Private entrance", "Wifi", "Lockbox"]
5th row["Extra pillows and blankets", "Host greets you", "Coffee", "Indoor fireplace: gas", "Refrigerator", "Frigidaire gas stove", "Free street parking", "Baking sheet", "First aid kit", "Shared hot tub", "Essentials", "Outdoor furniture", "Free parking on premises", "Dishwasher", "Books and reading material", "Ethernet connection", "BBQ grill", "Shared gym in building", "Hot water kettle", "Fire extinguisher", "Portable fans", "Microwave", "Oven", "Hot water", "Laundromat nearby", "Cleaning products", "Luggage dropoff allowed", "Paid dryer \u2013 In building", "Mountain view", "Hair dryer", "Theme room", "Shower gel", "Clothing storage", "Heating", "Coffee maker", "Carbon monoxide alarm", "Cleaning available during stay", "Central air conditioning", "Iron", "Toaster", "Paid washer \u2013 In building", "Outdoor dining area", "Exercise equipment", "Hangers", "Barbecue utensils", "Dining table", "Private patio or balcony", "Shared backyard \u2013 Fully fenced", "Wine glasses", "Dishes and silverware", "Smoke alarm", "Fire pit", "Sauna", "Elevator", "Movie theater", "Dedicated workspace", "Bathtub", "Cooking basics", "Shampoo", "Single level home", "Lock on bedroom door", "Breakfast", "Bed linens", "Kitchen", "Conditioner", "Long term stays allowed", "Blender", "Sun loungers", "EV charger", "Board games", "Freezer", "Wifi", "Body soap"]
ValueCountFrequency (%)
alarm 81564
 
2.2%
free 71451
 
2.0%
and 68918
 
1.9%
dryer 64034
 
1.8%
on 63875
 
1.7%
hot 60128
 
1.6%
parking 56793
 
1.6%
coffee 55098
 
1.5%
water 52263
 
1.4%
allowed 49792
 
1.4%
Other values (3269) 3028762
82.9%
2025-08-31T11:06:54.727286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3609171
 
11.9%
" 3338387
 
11.0%
e 2524250
 
8.3%
r 1862387
 
6.2%
a 1758439
 
5.8%
i 1660251
 
5.5%
, 1652410
 
5.5%
o 1491106
 
4.9%
n 1408244
 
4.7%
t 1251339
 
4.1%
Other values (76) 9702189
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30258173
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3609171
 
11.9%
" 3338387
 
11.0%
e 2524250
 
8.3%
r 1862387
 
6.2%
a 1758439
 
5.8%
i 1660251
 
5.5%
, 1652410
 
5.5%
o 1491106
 
4.9%
n 1408244
 
4.7%
t 1251339
 
4.1%
Other values (76) 9702189
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30258173
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3609171
 
11.9%
" 3338387
 
11.0%
e 2524250
 
8.3%
r 1862387
 
6.2%
a 1758439
 
5.8%
i 1660251
 
5.5%
, 1652410
 
5.5%
o 1491106
 
4.9%
n 1408244
 
4.7%
t 1251339
 
4.1%
Other values (76) 9702189
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30258173
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3609171
 
11.9%
" 3338387
 
11.0%
e 2524250
 
8.3%
r 1862387
 
6.2%
a 1758439
 
5.8%
i 1660251
 
5.5%
, 1652410
 
5.5%
o 1491106
 
4.9%
n 1408244
 
4.7%
t 1251339
 
4.1%
Other values (76) 9702189
32.1%

price
Text

Missing 

Distinct1803
Distinct (%)5.0%
Missing9016
Missing (%)19.8%
Memory size355.0 KiB
2025-08-31T11:06:55.121599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length6.817964565
Min length5

Characters and Unicode

Total characters248208
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique653 ?
Unique (%)1.8%

Sample

1st row$110.00
2nd row$118.00
3rd row$114.00
4th row$65.00
5th row$81.00
ValueCountFrequency (%)
100.00 356
 
1.0%
150.00 315
 
0.9%
90.00 312
 
0.9%
120.00 301
 
0.8%
50.00 286
 
0.8%
200.00 283
 
0.8%
80.00 273
 
0.7%
60.00 268
 
0.7%
95.00 267
 
0.7%
110.00 267
 
0.7%
Other values (1793) 33477
92.0%
2025-08-31T11:06:55.693079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 84037
33.9%
$ 36405
14.7%
. 36405
14.7%
1 19489
 
7.9%
2 12747
 
5.1%
5 10568
 
4.3%
3 9198
 
3.7%
4 8265
 
3.3%
9 8071
 
3.3%
6 7218
 
2.9%
Other values (3) 15805
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 248208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 84037
33.9%
$ 36405
14.7%
. 36405
14.7%
1 19489
 
7.9%
2 12747
 
5.1%
5 10568
 
4.3%
3 9198
 
3.7%
4 8265
 
3.3%
9 8071
 
3.3%
6 7218
 
2.9%
Other values (3) 15805
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 248208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 84037
33.9%
$ 36405
14.7%
. 36405
14.7%
1 19489
 
7.9%
2 12747
 
5.1%
5 10568
 
4.3%
3 9198
 
3.7%
4 8265
 
3.3%
9 8071
 
3.3%
6 7218
 
2.9%
Other values (3) 15805
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 248208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 84037
33.9%
$ 36405
14.7%
. 36405
14.7%
1 19489
 
7.9%
2 12747
 
5.1%
5 10568
 
4.3%
3 9198
 
3.7%
4 8265
 
3.3%
9 8071
 
3.3%
6 7218
 
2.9%
Other values (3) 15805
 
6.4%

minimum_nights
Real number (ℝ)

Distinct105
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.01578565
Minimum1
Maximum1124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:55.922351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median29
Q330
95-th percentile31
Maximum1124
Range1123
Interquartile range (IQR)28

Descriptive statistics

Standard deviation32.20993365
Coefficient of variation (CV)1.693852373
Kurtosis310.3626904
Mean19.01578565
Median Absolute Deviation (MAD)15
Skewness13.35808576
Sum863716
Variance1037.479826
MonotonicityNot monotonic
2025-08-31T11:06:56.115380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 17275
38.0%
1 9548
21.0%
2 6588
 
14.5%
31 4409
 
9.7%
3 3171
 
7.0%
5 803
 
1.8%
7 690
 
1.5%
4 683
 
1.5%
28 303
 
0.7%
14 222
 
0.5%
Other values (95) 1729
 
3.8%
ValueCountFrequency (%)
1 9548
21.0%
2 6588
14.5%
3 3171
 
7.0%
4 683
 
1.5%
5 803
 
1.8%
ValueCountFrequency (%)
1124 5
< 0.1%
1000 3
< 0.1%
950 1
 
< 0.1%
750 3
< 0.1%
730 1
 
< 0.1%

maximum_nights
Real number (ℝ)

Distinct271
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444.2291231
Minimum1
Maximum3650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:56.304609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q190
median365
Q3365
95-th percentile1125
Maximum3650
Range3649
Interquartile range (IQR)275

Descriptive statistics

Standard deviation394.3840121
Coefficient of variation (CV)0.8877941396
Kurtosis-0.5307419182
Mean444.2291231
Median Absolute Deviation (MAD)273
Skewness0.8675410841
Sum20177331
Variance155538.749
MonotonicityNot monotonic
2025-08-31T11:06:56.503040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 18519
40.8%
1125 9931
21.9%
90 2167
 
4.8%
30 1966
 
4.3%
28 1538
 
3.4%
180 1308
 
2.9%
60 1258
 
2.8%
730 822
 
1.8%
120 786
 
1.7%
29 711
 
1.6%
Other values (261) 6415
 
14.1%
ValueCountFrequency (%)
1 34
 
0.1%
2 38
 
0.1%
3 64
0.1%
4 60
0.1%
5 133
0.3%
ValueCountFrequency (%)
3650 1
 
< 0.1%
3000 2
 
< 0.1%
2345 1
 
< 0.1%
2000 1
 
< 0.1%
1125 9931
21.9%

minimum_minimum_nights
Real number (ℝ)

Distinct108
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.47028907
Minimum1
Maximum1124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:56.708247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median15
Q330
95-th percentile31
Maximum1124
Range1123
Interquartile range (IQR)28

Descriptive statistics

Standard deviation31.92682255
Coefficient of variation (CV)1.728550237
Kurtosis320.5844861
Mean18.47028907
Median Absolute Deviation (MAD)14
Skewness13.56075342
Sum838939
Variance1019.321998
MonotonicityNot monotonic
2025-08-31T11:06:56.902903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 16959
37.3%
1 11354
25.0%
2 6504
 
14.3%
31 4241
 
9.3%
3 2501
 
5.5%
5 622
 
1.4%
4 583
 
1.3%
7 561
 
1.2%
28 268
 
0.6%
14 195
 
0.4%
Other values (98) 1633
 
3.6%
ValueCountFrequency (%)
1 11354
25.0%
2 6504
14.3%
3 2501
 
5.5%
4 583
 
1.3%
5 622
 
1.4%
ValueCountFrequency (%)
1124 5
< 0.1%
1000 3
< 0.1%
950 1
 
< 0.1%
750 3
< 0.1%
730 1
 
< 0.1%

maximum_minimum_nights
Real number (ℝ)

Distinct118
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.33735497
Minimum1
Maximum1124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:57.096938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median29
Q330
95-th percentile31
Maximum1124
Range1123
Interquartile range (IQR)28

Descriptive statistics

Standard deviation41.55347171
Coefficient of variation (CV)1.947451864
Kurtosis145.3816513
Mean21.33735497
Median Absolute Deviation (MAD)19
Skewness9.586964789
Sum969164
Variance1726.691011
MonotonicityNot monotonic
2025-08-31T11:06:57.289260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 17211
37.9%
1 7421
16.3%
2 6232
 
13.7%
31 4188
 
9.2%
3 3941
 
8.7%
4 1544
 
3.4%
5 1109
 
2.4%
7 921
 
2.0%
28 332
 
0.7%
14 257
 
0.6%
Other values (108) 2265
 
5.0%
ValueCountFrequency (%)
1 7421
16.3%
2 6232
13.7%
3 3941
8.7%
4 1544
 
3.4%
5 1109
 
2.4%
ValueCountFrequency (%)
1124 6
< 0.1%
1000 3
< 0.1%
950 1
 
< 0.1%
750 5
< 0.1%
730 1
 
< 0.1%

minimum_maximum_nights
Real number (ℝ)

Distinct253
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean578.298056
Minimum1
Maximum3650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:57.478533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q1300
median365
Q31125
95-th percentile1125
Maximum3650
Range3649
Interquartile range (IQR)825

Descriptive statistics

Standard deviation440.8799265
Coefficient of variation (CV)0.7623749068
Kurtosis-1.535158729
Mean578.298056
Median Absolute Deviation (MAD)335
Skewness0.2698702717
Sum26266876
Variance194375.1096
MonotonicityNot monotonic
2025-08-31T11:06:57.683240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125 16688
36.7%
365 15922
35.1%
90 1543
 
3.4%
30 1353
 
3.0%
28 1001
 
2.2%
180 977
 
2.2%
60 904
 
2.0%
730 605
 
1.3%
120 573
 
1.3%
1 495
 
1.1%
Other values (243) 5360
 
11.8%
ValueCountFrequency (%)
1 495
1.1%
2 224
0.5%
3 93
 
0.2%
4 60
 
0.1%
5 100
 
0.2%
ValueCountFrequency (%)
3650 1
 
< 0.1%
3000 1
 
< 0.1%
2345 1
 
< 0.1%
2000 1
 
< 0.1%
1125 16688
36.7%

maximum_maximum_nights
Real number (ℝ)

Skewed 

Distinct253
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95161.14148
Minimum1
Maximum2147483647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:57.886796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1365
median365
Q31125
95-th percentile1125
Maximum2147483647
Range2147483646
Interquartile range (IQR)760

Descriptive statistics

Standard deviation14249893.38
Coefficient of variation (CV)149.7448765
Kurtosis22707.99978
Mean95161.14148
Median Absolute Deviation (MAD)335
Skewness150.6950559
Sum4322314207
Variance2.030594614 × 1014
MonotonicityNot monotonic
2025-08-31T11:06:58.082264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125 17567
38.7%
365 15902
35.0%
90 1528
 
3.4%
30 1332
 
2.9%
180 968
 
2.1%
60 881
 
1.9%
28 848
 
1.9%
730 606
 
1.3%
120 579
 
1.3%
29 461
 
1.0%
Other values (243) 4749
 
10.5%
ValueCountFrequency (%)
1 24
 
0.1%
2 34
 
0.1%
3 54
0.1%
4 51
0.1%
5 92
0.2%
ValueCountFrequency (%)
2147483647 2
< 0.1%
3650 1
< 0.1%
3000 1
< 0.1%
2345 1
< 0.1%
2000 1
< 0.1%

minimum_nights_avg_ntm
Real number (ℝ)

Distinct440
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.11794104
Minimum1
Maximum1124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:58.614509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median28
Q330
95-th percentile31
Maximum1124
Range1123
Interquartile range (IQR)28

Descriptive statistics

Standard deviation32.42154981
Coefficient of variation (CV)1.695870374
Kurtosis304.6313993
Mean19.11794104
Median Absolute Deviation (MAD)16
Skewness13.26914173
Sum868356
Variance1051.156892
MonotonicityNot monotonic
2025-08-31T11:06:58.816128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 16799
37.0%
1 7648
16.8%
2 5674
 
12.5%
31 4004
 
8.8%
3 2647
 
5.8%
5 642
 
1.4%
4 584
 
1.3%
7 554
 
1.2%
1.3 376
 
0.8%
2.3 264
 
0.6%
Other values (430) 6229
 
13.7%
ValueCountFrequency (%)
1 7648
16.8%
1.1 132
 
0.3%
1.2 63
 
0.1%
1.3 376
 
0.8%
1.4 128
 
0.3%
ValueCountFrequency (%)
1124 5
< 0.1%
1000 3
< 0.1%
950 1
 
< 0.1%
750 3
< 0.1%
730 1
 
< 0.1%

maximum_nights_avg_ntm
Real number (ℝ)

Skewed 

Distinct979
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31159.32937
Minimum1
Maximum982547328.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:59.018063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1365
median365
Q31125
95-th percentile1125
Maximum982547328.7
Range982547327.7
Interquartile range (IQR)760

Descriptive statistics

Standard deviation4988231.686
Coefficient of variation (CV)160.0879026
Kurtosis34104.98289
Mean31159.32937
Median Absolute Deviation (MAD)334
Skewness180.1141711
Sum1415287899
Variance2.488245535 × 1013
MonotonicityNot monotonic
2025-08-31T11:06:59.222494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1125 16688
36.7%
365 15786
34.8%
90 1526
 
3.4%
30 1302
 
2.9%
180 967
 
2.1%
60 878
 
1.9%
28 847
 
1.9%
730 604
 
1.3%
120 569
 
1.3%
29 459
 
1.0%
Other values (969) 5795
 
12.8%
ValueCountFrequency (%)
1 24
0.1%
1.3 1
 
< 0.1%
2 33
0.1%
3 54
0.1%
4 51
0.1%
ValueCountFrequency (%)
982547328.7 1
< 0.1%
405962983.4 1
< 0.1%
3650 1
< 0.1%
3000 1
< 0.1%
2345 1
< 0.1%

calendar_updated
Unsupported

Missing  Rejected  Unsupported 

Missing45421
Missing (%)100.0%
Memory size355.0 KiB

has_availability
Text

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing3505
Missing (%)7.7%
Memory size355.0 KiB
2025-08-31T11:06:59.333017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41916
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowt
3rd rowt
4th rowt
5th rowt
ValueCountFrequency (%)
t 41916
100.0%
2025-08-31T11:06:59.592216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 41916
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 41916
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 41916
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 41916
100.0%

availability_30
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.9615156
Minimum0
Maximum30
Zeros14424
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:06:59.783680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q326
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)26

Descriptive statistics

Standard deviation11.94591025
Coefficient of variation (CV)0.9216445529
Kurtosis-1.545647638
Mean12.9615156
Median Absolute Deviation (MAD)11
Skewness0.2562428772
Sum588725
Variance142.7047717
MonotonicityNot monotonic
2025-08-31T11:06:59.979248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 14424
31.8%
30 7541
16.6%
29 1781
 
3.9%
16 1193
 
2.6%
17 1041
 
2.3%
28 1034
 
2.3%
10 871
 
1.9%
11 868
 
1.9%
9 843
 
1.9%
24 814
 
1.8%
Other values (21) 15011
33.0%
ValueCountFrequency (%)
0 14424
31.8%
1 748
 
1.6%
2 699
 
1.5%
3 712
 
1.6%
4 775
 
1.7%
ValueCountFrequency (%)
30 7541
16.6%
29 1781
 
3.9%
28 1034
 
2.3%
27 743
 
1.6%
26 610
 
1.3%

availability_60
Real number (ℝ)

Zeros 

Distinct61
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.83472403
Minimum0
Maximum60
Zeros11189
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:00.169176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median31
Q355
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)54

Descriptive statistics

Standard deviation23.56442319
Coefficient of variation (CV)0.7898321155
Kurtosis-1.587076636
Mean29.83472403
Median Absolute Deviation (MAD)26
Skewness-0.03287856993
Sum1355123
Variance555.2820404
MonotonicityNot monotonic
2025-08-31T11:07:00.368182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11189
24.6%
60 7287
 
16.0%
59 1662
 
3.7%
58 885
 
1.9%
46 777
 
1.7%
47 738
 
1.6%
54 685
 
1.5%
15 671
 
1.5%
16 657
 
1.4%
57 641
 
1.4%
Other values (51) 20229
44.5%
ValueCountFrequency (%)
0 11189
24.6%
1 474
 
1.0%
2 253
 
0.6%
3 235
 
0.5%
4 230
 
0.5%
ValueCountFrequency (%)
60 7287
16.0%
59 1662
 
3.7%
58 885
 
1.9%
57 641
 
1.4%
56 516
 
1.1%

availability_90
Real number (ℝ)

Zeros 

Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.79155016
Minimum0
Maximum90
Zeros9364
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:00.570438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median56
Q384
95-th percentile90
Maximum90
Range90
Interquartile range (IQR)70

Descriptive statistics

Standard deviation34.38231141
Coefficient of variation (CV)0.6905250247
Kurtosis-1.446734041
Mean49.79155016
Median Absolute Deviation (MAD)32
Skewness-0.2965476018
Sum2261582
Variance1182.143338
MonotonicityNot monotonic
2025-08-31T11:07:00.765337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9364
20.6%
90 6903
 
15.2%
89 1848
 
4.1%
88 858
 
1.9%
76 748
 
1.6%
77 681
 
1.5%
87 649
 
1.4%
83 628
 
1.4%
84 625
 
1.4%
45 607
 
1.3%
Other values (81) 22510
49.6%
ValueCountFrequency (%)
0 9364
20.6%
1 272
 
0.6%
2 134
 
0.3%
3 123
 
0.3%
4 115
 
0.3%
ValueCountFrequency (%)
90 6903
15.2%
89 1848
 
4.1%
88 858
 
1.9%
87 649
 
1.4%
86 494
 
1.1%

availability_365
Real number (ℝ)

Zeros 

Distinct366
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206.1239074
Minimum0
Maximum365
Zeros7199
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:00.952063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q176
median241
Q3338
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)262

Descriptive statistics

Standard deviation135.3091409
Coefficient of variation (CV)0.6564456425
Kurtosis-1.409751566
Mean206.1239074
Median Absolute Deviation (MAD)114
Skewness-0.3304623915
Sum9362354
Variance18308.5636
MonotonicityNot monotonic
2025-08-31T11:07:01.158118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7199
 
15.8%
365 3893
 
8.6%
364 1762
 
3.9%
269 969
 
2.1%
363 543
 
1.2%
89 452
 
1.0%
351 378
 
0.8%
179 375
 
0.8%
358 365
 
0.8%
270 340
 
0.7%
Other values (356) 29145
64.2%
ValueCountFrequency (%)
0 7199
15.8%
1 107
 
0.2%
2 47
 
0.1%
3 45
 
0.1%
4 38
 
0.1%
ValueCountFrequency (%)
365 3893
8.6%
364 1762
3.9%
363 543
 
1.2%
362 330
 
0.7%
361 237
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:01.336904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters454210
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2025-06-17
2nd row2025-06-17
3rd row2025-06-17
4th row2025-06-17
5th row2025-06-17
ValueCountFrequency (%)
2025-06-17 30380
66.9%
2025-06-18 15041
33.1%
2025-08-31T11:07:01.655832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 90842
20.0%
0 90842
20.0%
- 90842
20.0%
5 45421
10.0%
6 45421
10.0%
1 45421
10.0%
7 30380
 
6.7%
8 15041
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 454210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 90842
20.0%
0 90842
20.0%
- 90842
20.0%
5 45421
10.0%
6 45421
10.0%
1 45421
10.0%
7 30380
 
6.7%
8 15041
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 454210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 90842
20.0%
0 90842
20.0%
- 90842
20.0%
5 45421
10.0%
6 45421
10.0%
1 45421
10.0%
7 30380
 
6.7%
8 15041
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 454210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 90842
20.0%
0 90842
20.0%
- 90842
20.0%
5 45421
10.0%
6 45421
10.0%
1 45421
10.0%
7 30380
 
6.7%
8 15041
 
3.3%

number_of_reviews
Real number (ℝ)

Zeros 

Distinct670
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.43768301
Minimum0
Maximum3119
Zeros12572
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:01.853718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q334
95-th percentile183
Maximum3119
Range3119
Interquartile range (IQR)34

Descriptive statistics

Standard deviation83.64449483
Coefficient of variation (CV)2.23423268
Kurtosis69.72789827
Mean37.43768301
Median Absolute Deviation (MAD)5
Skewness5.476829964
Sum1700457
Variance6996.401516
MonotonicityNot monotonic
2025-08-31T11:07:02.051032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12572
27.7%
1 3876
 
8.5%
2 2325
 
5.1%
3 1703
 
3.7%
4 1375
 
3.0%
5 1113
 
2.5%
6 971
 
2.1%
7 829
 
1.8%
8 705
 
1.6%
9 661
 
1.5%
Other values (660) 19291
42.5%
ValueCountFrequency (%)
0 12572
27.7%
1 3876
 
8.5%
2 2325
 
5.1%
3 1703
 
3.7%
4 1375
 
3.0%
ValueCountFrequency (%)
3119 1
< 0.1%
1293 1
< 0.1%
1292 1
< 0.1%
1261 1
< 0.1%
1235 1
< 0.1%

number_of_reviews_ltm
Real number (ℝ)

Zeros 

Distinct156
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.842495762
Minimum0
Maximum1060
Zeros21476
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:02.256975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile41
Maximum1060
Range1060
Interquartile range (IQR)8

Descriptive statistics

Standard deviation16.62983637
Coefficient of variation (CV)2.120477572
Kurtosis419.8634367
Mean7.842495762
Median Absolute Deviation (MAD)1
Skewness9.937552468
Sum356214
Variance276.5514578
MonotonicityNot monotonic
2025-08-31T11:07:02.452129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21476
47.3%
1 4343
 
9.6%
2 2743
 
6.0%
3 1848
 
4.1%
4 1342
 
3.0%
5 920
 
2.0%
6 731
 
1.6%
7 580
 
1.3%
8 505
 
1.1%
9 499
 
1.1%
Other values (146) 10434
23.0%
ValueCountFrequency (%)
0 21476
47.3%
1 4343
 
9.6%
2 2743
 
6.0%
3 1848
 
4.1%
4 1342
 
3.0%
ValueCountFrequency (%)
1060 1
< 0.1%
605 1
< 0.1%
440 1
< 0.1%
394 1
< 0.1%
344 1
< 0.1%

number_of_reviews_l30d
Real number (ℝ)

Zeros 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6867308073
Minimum0
Maximum47
Zeros32927
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:02.625206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum47
Range47
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.551320035
Coefficient of variation (CV)2.258992924
Kurtosis94.60418918
Mean0.6867308073
Median Absolute Deviation (MAD)0
Skewness5.655071804
Sum31192
Variance2.40659385
MonotonicityNot monotonic
2025-08-31T11:07:02.795246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 32927
72.5%
1 4883
 
10.8%
2 2953
 
6.5%
3 1947
 
4.3%
4 1206
 
2.7%
5 647
 
1.4%
6 381
 
0.8%
7 214
 
0.5%
8 123
 
0.3%
9 45
 
0.1%
Other values (17) 95
 
0.2%
ValueCountFrequency (%)
0 32927
72.5%
1 4883
 
10.8%
2 2953
 
6.5%
3 1947
 
4.3%
4 1206
 
2.7%
ValueCountFrequency (%)
47 2
< 0.1%
46 1
< 0.1%
43 1
< 0.1%
39 1
< 0.1%
38 1
< 0.1%

availability_eoy
Real number (ℝ)

Zeros 

Distinct199
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.5837168
Minimum0
Maximum198
Zeros8113
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:02.981240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median145
Q3185
95-th percentile198
Maximum198
Range198
Interquartile range (IQR)135

Descriptive statistics

Standard deviation74.07608001
Coefficient of variation (CV)0.6246732859
Kurtosis-1.244157557
Mean118.5837168
Median Absolute Deviation (MAD)52
Skewness-0.552296413
Sum5386191
Variance5487.26563
MonotonicityNot monotonic
2025-08-31T11:07:03.186917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8113
 
17.9%
198 3687
 
8.1%
197 3432
 
7.6%
196 663
 
1.5%
184 604
 
1.3%
179 597
 
1.3%
191 506
 
1.1%
153 485
 
1.1%
195 476
 
1.0%
89 473
 
1.0%
Other values (189) 26385
58.1%
ValueCountFrequency (%)
0 8113
17.9%
1 242
 
0.5%
2 82
 
0.2%
3 55
 
0.1%
4 60
 
0.1%
ValueCountFrequency (%)
198 3687
8.1%
197 3432
7.6%
196 663
 
1.5%
195 476
 
1.0%
194 333
 
0.7%

number_of_reviews_ly
Real number (ℝ)

Zeros 

Distinct158
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.412320292
Minimum0
Maximum507
Zeros25072
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:03.395537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile42
Maximum507
Range507
Interquartile range (IQR)5

Descriptive statistics

Standard deviation16.13525782
Coefficient of variation (CV)2.176816055
Kurtosis44.36099873
Mean7.412320292
Median Absolute Deviation (MAD)0
Skewness4.18043271
Sum336675
Variance260.3465448
MonotonicityNot monotonic
2025-08-31T11:07:03.598811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25072
55.2%
1 3339
 
7.4%
2 2246
 
4.9%
3 1603
 
3.5%
4 1114
 
2.5%
5 833
 
1.8%
6 602
 
1.3%
7 480
 
1.1%
8 409
 
0.9%
9 362
 
0.8%
Other values (148) 9361
 
20.6%
ValueCountFrequency (%)
0 25072
55.2%
1 3339
 
7.4%
2 2246
 
4.9%
3 1603
 
3.5%
4 1114
 
2.5%
ValueCountFrequency (%)
507 1
< 0.1%
387 1
< 0.1%
348 1
< 0.1%
224 1
< 0.1%
222 1
< 0.1%

estimated_occupancy_l365d
Real number (ℝ)

Zeros 

Distinct93
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.32064464
Minimum0
Maximum255
Zeros21476
Zeros (%)47.3%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:03.803020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q3120
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)120

Descriptive statistics

Standard deviation86.01652199
Coefficient of variation (CV)1.358427768
Kurtosis-0.02311073071
Mean63.32064464
Median Absolute Deviation (MAD)8
Skewness1.174269754
Sum2876087
Variance7398.842055
MonotonicityNot monotonic
2025-08-31T11:07:03.998970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21476
47.3%
255 3593
 
7.9%
60 2401
 
5.3%
120 1516
 
3.3%
6 1206
 
2.7%
180 924
 
2.0%
12 851
 
1.9%
62 697
 
1.5%
18 666
 
1.5%
240 620
 
1.4%
Other values (83) 11471
25.3%
ValueCountFrequency (%)
0 21476
47.3%
6 1206
 
2.7%
8 59
 
0.1%
10 61
 
0.1%
12 851
 
1.9%
ValueCountFrequency (%)
255 3593
7.9%
252 128
 
0.3%
250 6
 
< 0.1%
248 170
 
0.4%
246 105
 
0.2%

estimated_revenue_l365d
Real number (ℝ)

Missing  Zeros 

Distinct6217
Distinct (%)17.1%
Missing9016
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean15113.04895
Minimum0
Maximum1080000
Zeros14485
Zeros (%)31.9%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:04.199243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4272
Q320400
95-th percentile60945
Maximum1080000
Range1080000
Interquartile range (IQR)20400

Descriptive statistics

Standard deviation28220.12964
Coefficient of variation (CV)1.867269122
Kurtosis100.5469967
Mean15113.04895
Median Absolute Deviation (MAD)4272
Skewness6.286953901
Sum550190547
Variance796375716.7
MonotonicityNot monotonic
2025-08-31T11:07:04.411029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14485
31.9%
7200 67
 
0.1%
9000 60
 
0.1%
6000 56
 
0.1%
5400 53
 
0.1%
3000 50
 
0.1%
4800 49
 
0.1%
10800 46
 
0.1%
18000 45
 
0.1%
14400 44
 
0.1%
Other values (6207) 21450
47.2%
(Missing) 9016
19.8%
ValueCountFrequency (%)
0 14485
31.9%
96 1
 
< 0.1%
180 2
 
< 0.1%
192 1
 
< 0.1%
198 7
 
< 0.1%
ValueCountFrequency (%)
1080000 1
< 0.1%
559215 1
< 0.1%
544425 1
< 0.1%
482715 1
< 0.1%
478125 1
< 0.1%

first_review
Text

Missing 

Distinct4366
Distinct (%)13.3%
Missing12572
Missing (%)27.7%
Memory size355.0 KiB
2025-08-31T11:07:04.738771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters328490
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique691 ?
Unique (%)2.1%

Sample

1st row2014-06-09
2nd row2011-06-06
3rd row2009-08-13
4th row2009-05-26
5th row2018-12-29
ValueCountFrequency (%)
2025-05-18 88
 
0.3%
2025-05-26 72
 
0.2%
2025-06-01 72
 
0.2%
2025-05-04 61
 
0.2%
2025-03-31 61
 
0.2%
2025-04-27 58
 
0.2%
2025-03-17 57
 
0.2%
2025-04-20 56
 
0.2%
2025-05-31 55
 
0.2%
2024-07-07 55
 
0.2%
Other values (4356) 32214
98.1%
2025-08-31T11:07:05.255461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 78231
23.8%
0 75564
23.0%
- 65698
20.0%
1 39418
12.0%
3 13383
 
4.1%
4 12920
 
3.9%
5 12018
 
3.7%
8 8114
 
2.5%
7 7785
 
2.4%
9 7706
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 328490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 78231
23.8%
0 75564
23.0%
- 65698
20.0%
1 39418
12.0%
3 13383
 
4.1%
4 12920
 
3.9%
5 12018
 
3.7%
8 8114
 
2.5%
7 7785
 
2.4%
9 7706
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 328490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 78231
23.8%
0 75564
23.0%
- 65698
20.0%
1 39418
12.0%
3 13383
 
4.1%
4 12920
 
3.9%
5 12018
 
3.7%
8 8114
 
2.5%
7 7785
 
2.4%
9 7706
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 328490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 78231
23.8%
0 75564
23.0%
- 65698
20.0%
1 39418
12.0%
3 13383
 
4.1%
4 12920
 
3.9%
5 12018
 
3.7%
8 8114
 
2.5%
7 7785
 
2.4%
9 7706
 
2.3%

last_review
Text

Missing 

Distinct2910
Distinct (%)8.9%
Missing12572
Missing (%)27.7%
Memory size355.0 KiB
2025-08-31T11:07:05.610893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters328490
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique772 ?
Unique (%)2.4%

Sample

1st row2024-12-01
2nd row2022-08-21
3rd row2020-03-22
4th row2025-06-01
5th row2024-08-10
ValueCountFrequency (%)
2025-06-02 1175
 
3.6%
2025-06-01 1053
 
3.2%
2025-06-15 848
 
2.6%
2025-06-08 703
 
2.1%
2025-05-26 651
 
2.0%
2025-06-09 648
 
2.0%
2025-05-31 548
 
1.7%
2025-06-16 462
 
1.4%
2025-06-14 439
 
1.3%
2025-05-18 437
 
1.3%
Other values (2900) 25885
78.8%
2025-08-31T11:07:06.128854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 79187
24.1%
0 78334
23.8%
- 65698
20.0%
5 30484
 
9.3%
1 26439
 
8.0%
6 12870
 
3.9%
4 10705
 
3.3%
3 9667
 
2.9%
8 5551
 
1.7%
9 5263
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 328490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 79187
24.1%
0 78334
23.8%
- 65698
20.0%
5 30484
 
9.3%
1 26439
 
8.0%
6 12870
 
3.9%
4 10705
 
3.3%
3 9667
 
2.9%
8 5551
 
1.7%
9 5263
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 328490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 79187
24.1%
0 78334
23.8%
- 65698
20.0%
5 30484
 
9.3%
1 26439
 
8.0%
6 12870
 
3.9%
4 10705
 
3.3%
3 9667
 
2.9%
8 5551
 
1.7%
9 5263
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 328490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 79187
24.1%
0 78334
23.8%
- 65698
20.0%
5 30484
 
9.3%
1 26439
 
8.0%
6 12870
 
3.9%
4 10705
 
3.3%
3 9667
 
2.9%
8 5551
 
1.7%
9 5263
 
1.6%

review_scores_rating
Real number (ℝ)

Missing 

Distinct140
Distinct (%)0.4%
Missing12572
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean4.778346982
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:06.360117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.11
Q14.74
median4.91
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.4324554535
Coefficient of variation (CV)0.09050314996
Kurtosis35.5971438
Mean4.778346982
Median Absolute Deviation (MAD)0.09
Skewness-5.170736754
Sum156963.92
Variance0.1870177192
MonotonicityNot monotonic
2025-08-31T11:07:06.564324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 10842
23.9%
4.94 793
 
1.7%
4.88 774
 
1.7%
4 754
 
1.7%
4.5 725
 
1.6%
4.95 722
 
1.6%
4.96 713
 
1.6%
4.92 713
 
1.6%
4.67 696
 
1.5%
4.93 692
 
1.5%
Other values (130) 15425
34.0%
(Missing) 12572
27.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 165
0.4%
1.5 9
 
< 0.1%
1.67 1
 
< 0.1%
2 74
0.2%
ValueCountFrequency (%)
5 10842
23.9%
4.99 274
 
0.6%
4.98 527
 
1.2%
4.97 576
 
1.3%
4.96 713
 
1.6%

review_scores_accuracy
Real number (ℝ)

Missing 

Distinct141
Distinct (%)0.4%
Missing12580
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean4.799845315
Minimum0
Maximum5.07
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:06.770956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2
Q14.78
median4.92
Q35
95-th percentile5
Maximum5.07
Range5.07
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.4161874115
Coefficient of variation (CV)0.08670850499
Kurtosis39.59080757
Mean4.799845315
Median Absolute Deviation (MAD)0.08
Skewness-5.468380319
Sum157631.72
Variance0.1732119615
MonotonicityNot monotonic
2025-08-31T11:07:06.973806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 11094
24.4%
4.95 884
 
1.9%
4.96 881
 
1.9%
4.92 864
 
1.9%
4.94 830
 
1.8%
4.93 815
 
1.8%
4.88 806
 
1.8%
4.91 792
 
1.7%
4.89 751
 
1.7%
4.97 736
 
1.6%
Other values (131) 14388
31.7%
(Missing) 12580
27.7%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 139
0.3%
1.5 4
 
< 0.1%
1.67 1
 
< 0.1%
2 84
0.2%
ValueCountFrequency (%)
5.07 1
 
< 0.1%
5 11094
24.4%
4.99 302
 
0.7%
4.98 623
 
1.4%
4.97 736
 
1.6%

review_scores_cleanliness
Real number (ℝ)

Missing 

Distinct165
Distinct (%)0.5%
Missing12580
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean4.748008891
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:07.175573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14.69
median4.88
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.4464715539
Coefficient of variation (CV)0.09403342834
Kurtosis29.58645737
Mean4.748008891
Median Absolute Deviation (MAD)0.12
Skewness-4.610401337
Sum155929.36
Variance0.1993368484
MonotonicityNot monotonic
2025-08-31T11:07:07.796701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 9868
21.7%
4 893
 
2.0%
4.5 874
 
1.9%
4.88 767
 
1.7%
4.67 765
 
1.7%
4.95 701
 
1.5%
4.93 695
 
1.5%
4.92 680
 
1.5%
4.75 676
 
1.5%
4.94 665
 
1.5%
Other values (155) 16257
35.8%
(Missing) 12580
27.7%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 148
0.3%
1.5 5
 
< 0.1%
2 90
0.2%
2.22 1
 
< 0.1%
ValueCountFrequency (%)
5 9868
21.7%
4.99 263
 
0.6%
4.98 469
 
1.0%
4.97 557
 
1.2%
4.96 655
 
1.4%

review_scores_checkin
Real number (ℝ)

Missing 

Distinct130
Distinct (%)0.4%
Missing12588
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean4.853983797
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:08.002803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.4
Q14.86
median4.97
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.3824282682
Coefficient of variation (CV)0.07878647401
Kurtosis53.9335499
Mean4.853983797
Median Absolute Deviation (MAD)0.03
Skewness-6.492814736
Sum159370.85
Variance0.1462513803
MonotonicityNot monotonic
2025-08-31T11:07:08.205899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 13859
30.5%
4.96 1117
 
2.5%
4.98 1088
 
2.4%
4.97 1071
 
2.4%
4.95 1040
 
2.3%
4.94 1038
 
2.3%
4.93 855
 
1.9%
4.92 838
 
1.8%
4.91 745
 
1.6%
4.88 673
 
1.5%
Other values (120) 10509
23.1%
(Missing) 12588
27.7%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 134
0.3%
1.5 4
 
< 0.1%
2 46
 
0.1%
2.25 1
 
< 0.1%
ValueCountFrequency (%)
5 13859
30.5%
4.99 656
 
1.4%
4.98 1088
 
2.4%
4.97 1071
 
2.4%
4.96 1117
 
2.5%

review_scores_communication
Real number (ℝ)

Missing 

Distinct131
Distinct (%)0.4%
Missing12582
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean4.854049149
Minimum0
Maximum5.07
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:08.405466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.38
Q14.87
median4.98
Q35
95-th percentile5
Maximum5.07
Range5.07
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.3934925272
Coefficient of variation (CV)0.08106480078
Kurtosis50.56903746
Mean4.854049149
Median Absolute Deviation (MAD)0.02
Skewness-6.33235798
Sum159402.12
Variance0.154836369
MonotonicityNot monotonic
2025-08-31T11:07:08.616970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 14544
32.0%
4.98 1127
 
2.5%
4.97 1087
 
2.4%
4.96 1067
 
2.3%
4.94 1013
 
2.2%
4.95 952
 
2.1%
4.93 786
 
1.7%
4.99 770
 
1.7%
4.92 758
 
1.7%
4.89 637
 
1.4%
Other values (121) 10098
22.2%
(Missing) 12582
27.7%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 138
0.3%
1.5 3
 
< 0.1%
1.67 1
 
< 0.1%
2 59
0.1%
ValueCountFrequency (%)
5.07 1
 
< 0.1%
5 14544
32.0%
4.99 770
 
1.7%
4.98 1127
 
2.5%
4.97 1087
 
2.4%

review_scores_location
Real number (ℝ)

Missing 

Distinct153
Distinct (%)0.5%
Missing12589
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean4.791057809
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:08.826419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.17
Q14.75
median4.91
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.3961850747
Coefficient of variation (CV)0.08269260996
Kurtosis38.99554344
Mean4.791057809
Median Absolute Deviation (MAD)0.09
Skewness-5.25573745
Sum157300.01
Variance0.1569626135
MonotonicityNot monotonic
2025-08-31T11:07:09.034244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 10719
23.6%
4.88 858
 
1.9%
4.92 799
 
1.8%
4 777
 
1.7%
4.95 770
 
1.7%
4.94 770
 
1.7%
4.93 760
 
1.7%
4.9 747
 
1.6%
4.67 728
 
1.6%
4.91 724
 
1.6%
Other values (143) 15180
33.4%
(Missing) 12589
27.7%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 111
0.2%
1.5 4
 
< 0.1%
1.67 1
 
< 0.1%
2 56
0.1%
ValueCountFrequency (%)
5 10719
23.6%
4.99 215
 
0.5%
4.98 526
 
1.2%
4.97 595
 
1.3%
4.96 677
 
1.5%

review_scores_value
Real number (ℝ)

Missing 

Distinct155
Distinct (%)0.5%
Missing12590
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean4.70604642
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:09.241211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14.65
median4.82
Q34.97
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.4578166219
Coefficient of variation (CV)0.09728264047
Kurtosis28.1426676
Mean4.70604642
Median Absolute Deviation (MAD)0.16
Skewness-4.506424521
Sum154504.21
Variance0.2095960593
MonotonicityNot monotonic
2025-08-31T11:07:09.448130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 7959
17.5%
4.5 1013
 
2.2%
4 991
 
2.2%
4.67 985
 
2.2%
4.75 871
 
1.9%
4.8 858
 
1.9%
4.88 807
 
1.8%
4.83 773
 
1.7%
4.86 744
 
1.6%
4.89 687
 
1.5%
Other values (145) 17143
37.7%
(Missing) 12590
27.7%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 168
0.4%
1.5 8
 
< 0.1%
1.67 1
 
< 0.1%
2 84
0.2%
ValueCountFrequency (%)
5 7959
17.5%
4.99 15
 
< 0.1%
4.98 88
 
0.2%
4.97 161
 
0.4%
4.96 258
 
0.6%

license
Text

Missing 

Distinct7487
Distinct (%)58.1%
Missing32539
Missing (%)71.6%
Memory size355.0 KiB
2025-08-31T11:07:09.839884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length12
Mean length17.78473839
Min length5

Characters and Unicode

Total characters229103
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6442 ?
Unique (%)50.0%

Sample

1st row228269
2nd rowHSR24-001423
3rd rowExempt - This listing is a transient occupancy residential structure
4th rowExempt - This listing is a transient occupancy residential structure
5th rowHSR23-001393
ValueCountFrequency (%)
exempt 3737
 
11.7%
2285
 
7.2%
this 2283
 
7.1%
listing 2283
 
7.1%
is 2283
 
7.1%
a 2283
 
7.1%
or 1053
 
3.3%
motel 1053
 
3.3%
hotel 1053
 
3.3%
transient 745
 
2.3%
Other values (7488) 12889
40.3%
2025-08-31T11:07:10.421895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21879
 
9.5%
19067
 
8.3%
2 13927
 
6.1%
t 12379
 
5.4%
i 11389
 
5.0%
- 10925
 
4.8%
e 9823
 
4.3%
s 9666
 
4.2%
R 8477
 
3.7%
S 7509
 
3.3%
Other values (42) 104062
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 229103
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21879
 
9.5%
19067
 
8.3%
2 13927
 
6.1%
t 12379
 
5.4%
i 11389
 
5.0%
- 10925
 
4.8%
e 9823
 
4.3%
s 9666
 
4.2%
R 8477
 
3.7%
S 7509
 
3.3%
Other values (42) 104062
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 229103
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21879
 
9.5%
19067
 
8.3%
2 13927
 
6.1%
t 12379
 
5.4%
i 11389
 
5.0%
- 10925
 
4.8%
e 9823
 
4.3%
s 9666
 
4.2%
R 8477
 
3.7%
S 7509
 
3.3%
Other values (42) 104062
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 229103
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21879
 
9.5%
19067
 
8.3%
2 13927
 
6.1%
t 12379
 
5.4%
i 11389
 
5.0%
- 10925
 
4.8%
e 9823
 
4.3%
s 9666
 
4.2%
R 8477
 
3.7%
S 7509
 
3.3%
Other values (42) 104062
45.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:10.563218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45421
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowf
3rd rowf
4th rowf
5th rowt
ValueCountFrequency (%)
f 33968
74.8%
t 11453
 
25.2%
2025-08-31T11:07:10.832110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 33968
74.8%
t 11453
 
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 33968
74.8%
t 11453
 
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 33968
74.8%
t 11453
 
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 33968
74.8%
t 11453
 
25.2%
Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.53589749
Minimum1
Maximum599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:11.027169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q310
95-th percentile90
Maximum599
Range598
Interquartile range (IQR)9

Descriptive statistics

Standard deviation71.8369798
Coefficient of variation (CV)3.498117374
Kurtosis52.65091451
Mean20.53589749
Median Absolute Deviation (MAD)1
Skewness7.003181582
Sum932761
Variance5160.551667
MonotonicityNot monotonic
2025-08-31T11:07:11.225593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17182
37.8%
2 5708
 
12.6%
3 3135
 
6.9%
4 2428
 
5.3%
5 1590
 
3.5%
6 1308
 
2.9%
8 992
 
2.2%
7 973
 
2.1%
599 599
 
1.3%
10 570
 
1.3%
Other values (67) 10936
24.1%
ValueCountFrequency (%)
1 17182
37.8%
2 5708
 
12.6%
3 3135
 
6.9%
4 2428
 
5.3%
5 1590
 
3.5%
ValueCountFrequency (%)
599 599
1.3%
152 152
 
0.3%
146 146
 
0.3%
138 138
 
0.3%
135 270
0.6%
Distinct68
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.28081724
Minimum0
Maximum599
Zeros8536
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:11.424914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q36
95-th percentile56
Maximum599
Range599
Interquartile range (IQR)5

Descriptive statistics

Standard deviation70.13400477
Coefficient of variation (CV)4.307769305
Kurtosis59.9543973
Mean16.28081724
Median Absolute Deviation (MAD)1
Skewness7.62401543
Sum739491
Variance4918.778626
MonotonicityNot monotonic
2025-08-31T11:07:11.625328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15486
34.1%
0 8536
18.8%
2 4488
 
9.9%
3 2401
 
5.3%
4 1828
 
4.0%
5 1102
 
2.4%
6 1002
 
2.2%
7 786
 
1.7%
8 782
 
1.7%
599 599
 
1.3%
Other values (58) 8411
18.5%
ValueCountFrequency (%)
0 8536
18.8%
1 15486
34.1%
2 4488
 
9.9%
3 2401
 
5.3%
4 1828
 
4.0%
ValueCountFrequency (%)
599 599
1.3%
135 270
0.6%
125 125
 
0.3%
123 123
 
0.3%
100 100
 
0.2%
Distinct43
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.366306334
Minimum0
Maximum133
Zeros30955
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:11.828289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile13
Maximum133
Range133
Interquartile range (IQR)1

Descriptive statistics

Standard deviation13.69204884
Coefficient of variation (CV)4.067380531
Kurtosis45.05329192
Mean3.366306334
Median Absolute Deviation (MAD)0
Skewness6.35821388
Sum152901
Variance187.4722016
MonotonicityNot monotonic
2025-08-31T11:07:12.020532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 30955
68.2%
1 5702
 
12.6%
2 1817
 
4.0%
3 1196
 
2.6%
4 1022
 
2.3%
6 507
 
1.1%
5 476
 
1.0%
9 352
 
0.8%
8 296
 
0.7%
7 261
 
0.6%
Other values (33) 2837
 
6.2%
ValueCountFrequency (%)
0 30955
68.2%
1 5702
 
12.6%
2 1817
 
4.0%
3 1196
 
2.6%
4 1022
 
2.3%
ValueCountFrequency (%)
133 152
0.3%
109 110
0.2%
89 90
0.2%
85 146
0.3%
80 80
0.2%
Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1925320887
Minimum0
Maximum38
Zeros44604
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:12.181240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum38
Range38
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.104226665
Coefficient of variation (CV)10.92922577
Kurtosis181.415632
Mean0.1925320887
Median Absolute Deviation (MAD)0
Skewness13.01477152
Sum8745
Variance4.427769857
MonotonicityNot monotonic
2025-08-31T11:07:12.334087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 44604
98.2%
1 248
 
0.5%
24 146
 
0.3%
2 99
 
0.2%
25 70
 
0.2%
3 48
 
0.1%
38 44
 
0.1%
8 41
 
0.1%
9 31
 
0.1%
4 30
 
0.1%
Other values (3) 60
 
0.1%
ValueCountFrequency (%)
0 44604
98.2%
1 248
 
0.5%
2 99
 
0.2%
3 48
 
0.1%
4 30
 
0.1%
ValueCountFrequency (%)
38 44
 
0.1%
25 70
0.2%
24 146
0.3%
13 17
 
< 0.1%
9 31
 
0.1%

reviews_per_month
Real number (ℝ)

Missing 

Distinct921
Distinct (%)2.8%
Missing12572
Missing (%)27.7%
Infinite0
Infinite (%)0.0%
Mean1.363129471
Minimum0.01
Maximum74.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.0 KiB
2025-08-31T11:07:12.520770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.2
median0.7
Q32.02
95-th percentile4.59
Maximum74.26
Range74.25
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.74808719
Coefficient of variation (CV)1.282407304
Kurtosis161.9133926
Mean1.363129471
Median Absolute Deviation (MAD)0.61
Skewness6.329064335
Sum44777.44
Variance3.055808825
MonotonicityNot monotonic
2025-08-31T11:07:12.722378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 671
 
1.5%
0.03 611
 
1.3%
0.01 565
 
1.2%
0.02 561
 
1.2%
0.04 550
 
1.2%
0.09 503
 
1.1%
0.06 494
 
1.1%
0.05 486
 
1.1%
0.08 429
 
0.9%
0.07 405
 
0.9%
Other values (911) 27574
60.7%
(Missing) 12572
27.7%
ValueCountFrequency (%)
0.01 565
1.2%
0.02 561
1.2%
0.03 611
1.3%
0.04 550
1.2%
0.05 486
1.1%
ValueCountFrequency (%)
74.26 1
< 0.1%
51.16 1
< 0.1%
49.87 1
< 0.1%
48.73 1
< 0.1%
40.3 1
< 0.1%